Soham Patankar
fbe1e9977f
All-platform progressive-disclosure skill split + generator (addresses #1106). Splits each platform's skill into a lean core (~615 lines, full default pipeline inline) + on-demand references/, generated from a single source via tools/skillgen with a CI/pre-commit drift gate. 13 hosts split, aider/devin stay monoliths. Also fixes the stale bare-path bugs across the previously hand-maintained variants and moves the always-on blocks into packaged markdown. Verified: all 5 generator guards pass, byte-verbatim load-bearing slices, lean cores self-sufficient on the default path across all 13 split hosts, references gated to non-default branches, description preserves the graphify-out-query-first clause. Supersedes #1119 (Claude-first subset). Known follow-up applied on top: harden _always_on() against a missing packaged file so a partial install can't brick the CLI.
2.4 KiB
graphify reference: add a URL and watch a folder
Load this when the user ran /graphify add <url> or passed --watch. Neither is part of the default build.
For /graphify add
Fetch a URL and add it to the corpus, then update the graph.
$(cat graphify-out/.graphify_python) -c "
import sys
from graphify.ingest import ingest
from pathlib import Path
try:
out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR')
print(f'Saved to {out}')
except ValueError as e:
print(f'error: {e}', file=sys.stderr)
sys.exit(1)
except RuntimeError as e:
print(f'error: {e}', file=sys.stderr)
sys.exit(1)
"
Replace URL with the actual URL, AUTHOR with the user's name if provided, CONTRIBUTOR likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the --update pipeline on ./raw to merge the new file into the existing graph.
Supported URL types (auto-detected):
- YouTube / any video URL → audio downloaded via yt-dlp, transcribed to
.txton next run (requirespip install 'graphifyy[video]') - Twitter/X → fetched via oEmbed, saved as
.mdwith tweet text and author - arXiv → abstract + metadata saved as
.md - PDF → downloaded as
.pdf - Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
- Any webpage → converted to markdown via html2text
For --watch
Start a background watcher that monitors a folder and auto-updates the graph when files change.
python3 -m graphify.watch INPUT_PATH --debounce 3
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
- Code files only (.py, .ts, .go, etc.): re-runs AST extraction + rebuild + cluster immediately, no LLM needed.
graph.jsonandGRAPH_REPORT.mdare updated automatically. - Docs, papers, or images: writes a
graphify-out/needs_updateflag and prints a notification to run/graphify --update(LLM semantic re-extraction required).
Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file.
Press Ctrl+C to stop.
For agentic workflows: run --watch in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual /graphify --update after those waves.