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Fix remaining #1392 skill bugs: --directed propagation, content-only semantic scope, cache staleness, video update, transcribe robustness

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Closes the non-crash tier of #1392 in the Claude-path skill fragments:
- #6/#7: build_from_json/build_merge now take directed=IS_DIRECTED in Step 4, Step 5 rebuild, and the --update merge/diff, with prose telling the agent to substitute IS_DIRECTED like INPUT_PATH (a --directed run no longer silently rebuilds undirected and collapses reciprocal edges)
- #10: semantic extraction only flattens document/paper/image, not code (AST already covers code) so subagents stop re-reading every source file
- #12: .graphify_cached.json is deleted on a cache miss so Part C never merges a stale cache from a prior run
- #11: --update now transcribes changed video files and moves transcripts to documents before the semantic pipeline
- #4/#5/#23: transcribe writes via write_text (no shell redirect), uses GRAPHIFY_WHISPER_MODEL/PROMPT env, status to stderr
- #2/#3: add-watch and exports use $(cat graphify-out/.graphify_python) explicitly; MCP Desktop config documents the absolute interpreter path
- #21: extraction-spec example id namespaced (auth_session_validatetoken)
- #22: query term split keeps tokens >= 3 chars

aider/devin monoliths are pinned by the roundtrip invariant and excluded; their own Step 1 already instructs replacing python3 with the resolved interpreter.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Safi
2026-06-19 15:37:11 +01:00
co-authored by Claude Opus 4.8
parent 30fe8bab61
commit 382b669481
190 changed files with 892 additions and 487 deletions
Download Patch File Download Diff File Expand all files Collapse all files
graphify/skill-amp.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-claw.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-codex.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-copilot.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-droid.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-kilo.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-kiro.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-opencode.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -369,7 +376,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -387,7 +394,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -450,7 +457,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-pi.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-trae.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -375,7 +382,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -393,7 +400,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -456,7 +463,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-vscode.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -373,7 +380,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -391,7 +398,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -454,7 +461,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill-windows.md
+11 -4
View File
@@ -241,12 +241,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -399,7 +406,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -417,7 +424,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -480,7 +487,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skill.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
graphify/skills/amp/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/amp/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/amp/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/amp/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/amp/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/amp/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/claude/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/claude/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/claude/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/claude/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/claude/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/claude/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/claw/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/claw/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/claw/references/extraction-spec.md
+1 -1
View File
@@ -25,7 +25,7 @@ Rules:
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the immediate parent directory + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent. `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`. Top-level files use just the filename stem. This must match the AST extractor's ID. Never append chunk or sequence suffixes — IDs must be deterministic from the label alone.
Output exactly this JSON (no other text):
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE: set source_file to the FILE_LIST path for that file VERBATIM (absolute, no shortening to basename, no re-relativizing, no separator change). Keeps full build and --update on one base so build_merge's replace matches instead of duplicating.
```
graphify/skills/claw/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/claw/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/claw/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/codex/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/codex/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/codex/references/extraction-spec.md
+1 -1
View File
@@ -25,7 +25,7 @@ Rules:
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the immediate parent directory + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent. `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`. Top-level files use just the filename stem. This must match the AST extractor's ID. Never append chunk or sequence suffixes — IDs must be deterministic from the label alone.
Output exactly this JSON (no other text):
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE: set source_file to the FILE_LIST path for that file VERBATIM (absolute, no shortening to basename, no re-relativizing, no separator change). Keeps full build and --update on one base so build_merge's replace matches instead of duplicating.
```
graphify/skills/codex/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/codex/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/codex/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/copilot/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/copilot/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/copilot/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/copilot/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/copilot/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/copilot/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/droid/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/droid/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/droid/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/droid/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/droid/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/droid/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/kilo/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/kilo/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/kilo/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/kilo/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/kilo/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/kilo/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/kiro/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/kiro/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/kiro/references/extraction-spec.md
+1 -1
View File
@@ -25,7 +25,7 @@ Rules:
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the immediate parent directory + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent. `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`. Top-level files use just the filename stem. This must match the AST extractor's ID. Never append chunk or sequence suffixes — IDs must be deterministic from the label alone.
Output exactly this JSON (no other text):
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE: set source_file to the FILE_LIST path for that file VERBATIM (absolute, no shortening to basename, no re-relativizing, no separator change). Keeps full build and --update on one base so build_merge's replace matches instead of duplicating.
```
graphify/skills/kiro/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/kiro/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/kiro/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/opencode/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/opencode/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/opencode/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/opencode/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/opencode/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/opencode/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/pi/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/pi/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/pi/references/extraction-spec.md
+1 -1
View File
@@ -25,7 +25,7 @@ Rules:
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the immediate parent directory + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent. `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`. Top-level files use just the filename stem. This must match the AST extractor's ID. Never append chunk or sequence suffixes — IDs must be deterministic from the label alone.
Output exactly this JSON (no other text):
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE: set source_file to the FILE_LIST path for that file VERBATIM (absolute, no shortening to basename, no re-relativizing, no separator change). Keeps full build and --update on one base so build_merge's replace matches instead of duplicating.
```
graphify/skills/pi/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/pi/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/pi/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/trae/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/trae/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/trae/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/trae/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/trae/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/trae/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/vscode/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/vscode/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/vscode/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/vscode/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/vscode/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/vscode/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
graphify/skills/windows/references/add-watch.md
+1 -1
View File
@@ -41,7 +41,7 @@ Supported URL types (auto-detected):
Start a background watcher that monitors a folder and auto-updates the graph when files change.
```bash
python3 -m graphify.watch INPUT_PATH --debounce 3
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
```
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graphify/skills/windows/references/exports.md
+3 -3
View File
@@ -59,17 +59,17 @@ graphify export graphml
### Step 7d - MCP server (only if --mcp flag)
```bash
python3 -m graphify.serve graphify-out/graph.json
$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
```
This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
To configure in Claude Desktop, add to `claude_desktop_config.json`:
To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
```json
{
"mcpServers": {
"graphify": {
"command": "python3",
"command": "<absolute path from: cat graphify-out/.graphify_python>",
"args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
}
}
graphify/skills/windows/references/extraction-spec.md
+1 -1
View File
@@ -61,7 +61,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
graphify/skills/windows/references/query.md
+1 -1
View File
@@ -88,7 +88,7 @@ G = json_graph.node_link_graph(data, edges='links')
question = 'QUESTION'
mode = 'MODE' # 'bfs' or 'dfs'
terms = [t.lower() for t in question.split() if len(t) > 3]
terms = [t.lower() for t in question.split() if len(t) >= 3] # match the vocab threshold; keeps api/jwt/ios (#1392)
# Find best-matching start nodes
scored = []
graphify/skills/windows/references/transcribe.md
+10 -6
View File
@@ -19,14 +19,15 @@ Read the top god node labels from detect output or analysis, then compose a shor
- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
Set it as `WHISPER_PROMPT` to use in the next command.
**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
**Step 2 - Transcribe:**
```bash
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
export GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed (must be exported)
export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
$(cat graphify-out/.graphify_python) -c "
import json, os
import json, os, sys
from pathlib import Path
from graphify.transcribe import transcribe_all
@@ -35,8 +36,11 @@ video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
"
```
After transcription:
@@ -45,4 +49,4 @@ After transcription:
- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
- If transcription fails for a file, print a warning and continue with the rest
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, set `GRAPHIFY_WHISPER_MODEL=<name>` in the environment before running the command above.
**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.
graphify/skills/windows/references/update.md
+6 -2
View File
@@ -63,7 +63,7 @@ print('code_only:', code_only)
If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.
If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
If no new files exist (only deletions), create an empty extraction so the merge step can prune:
@@ -106,11 +106,15 @@ prune = list(deleted) or None
# Pass root= so prune_sources (absolute paths from detect_incremental) are
# relativized to match the graph's relative source_file values; without it
# nothing is pruned and stale nodes accumulate on every update (#1361).
# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
# False. Without it a --directed --update silently rebuilds undirected and collapses
# reciprocal A<->B edges (#1392).
G = build_merge(
[new_extraction],
graph_path='graphify-out/graph.json',
prune_sources=prune,
root='INPUT_PATH',
directed=IS_DIRECTED,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
@@ -156,7 +160,7 @@ from pathlib import Path
# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)
G_new = build_from_json(new_extract, directed=IS_DIRECTED)
if old_data:
G_old = json_graph.node_link_graph(old_data, edges='links')
tools/skillgen/expected/graphify__skill-amp.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-claw.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-codex.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-copilot.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-droid.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -374,7 +381,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -392,7 +399,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -455,7 +462,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-kilo.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-kiro.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-opencode.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -369,7 +376,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -387,7 +394,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -450,7 +457,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
tools/skillgen/expected/graphify__skill-pi.md
+11 -4
View File
@@ -219,12 +219,19 @@ from graphify.cache import check_semantic_cache
from pathlib import Path
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
all_files = [f for files in detect['files'].values() for f in files]
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)
# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"
@@ -377,7 +384,7 @@ print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(s
### Step 4 - Build graph, cluster, analyze, generate outputs
**Before starting:** note whether `--directed` was given. If so, pass `directed=True` to `build_from_json()` in the code block below. This builds a `DiGraph` that preserves edge direction (source→target) instead of the default undirected `Graph`.
**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
```bash
mkdir -p graphify-out
@@ -395,7 +402,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
@@ -458,7 +465,7 @@ detection = json.loads(Path('graphify-out/.graphify_detect.json').read_text(enc
analysis = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH')
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}

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