diff --git a/graphify/skill-aider.md b/graphify/skill-aider.md index fa7007f..de57044 100644 --- a/graphify/skill-aider.md +++ b/graphify/skill-aider.md @@ -235,7 +235,7 @@ Process each file one at a time. For each file: - INFERRED: reasonable inference (shared structure, implied dependency) - AMBIGUOUS: uncertain — flag it, do not omit - Code files: semantic edges AST cannot find. Do not re-extract imports. - - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. When adding `calls` edges: source is caller, target is callee. + - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms). Do NOT invent file_types like `concept`. When adding `calls` edges: source is caller, target is callee. - Image files: use vision — understand what the image IS, not just OCR - DEEP_MODE (if --mode deep): be aggressive with INFERRED edges - Semantic similarity: if two concepts solve the same problem without a structural link, add `semantically_similar_to` INFERRED edge (confidence 0.6-0.95). Non-obvious cross-file links only. @@ -244,7 +244,7 @@ Process each file one at a time. For each file: 3. Accumulate results across all files Schema for each file's output: -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} After processing all files, write the accumulated result to `.graphify_semantic_new.json`. @@ -254,6 +254,30 @@ For the accumulated result: If more than half the chunks failed, stop and tell the user. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-claw.md b/graphify/skill-claw.md index 3d84acb..865eae2 100644 --- a/graphify/skill-claw.md +++ b/graphify/skill-claw.md @@ -235,7 +235,7 @@ Process each file one at a time. For each file: - INFERRED: reasonable inference (shared structure, implied dependency) - AMBIGUOUS: uncertain — flag it, do not omit - Code files: semantic edges AST cannot find. Do not re-extract imports. - - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. When adding `calls` edges: source is caller, target is callee. + - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms). Do NOT invent file_types like `concept`. When adding `calls` edges: source is caller, target is callee. - Image files: use vision — understand what the image IS, not just OCR - DEEP_MODE (if --mode deep): be aggressive with INFERRED edges - Semantic similarity: if two concepts solve the same problem without a structural link, add `semantically_similar_to` INFERRED edge (confidence 0.6-0.95). Non-obvious cross-file links only. @@ -244,7 +244,7 @@ Process each file one at a time. For each file: 3. Accumulate results across all files Schema for each file's output: -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} After processing all files, write the accumulated result to `.graphify_semantic_new.json`. @@ -254,6 +254,30 @@ For the accumulated result: If more than half the chunks failed, stop and tell the user. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-codex.md b/graphify/skill-codex.md index b2e79e2..febdb6e 100644 --- a/graphify/skill-codex.md +++ b/graphify/skill-codex.md @@ -263,7 +263,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -300,7 +300,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -313,6 +313,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-copilot.md b/graphify/skill-copilot.md index 397669a..826798c 100644 --- a/graphify/skill-copilot.md +++ b/graphify/skill-copilot.md @@ -259,7 +259,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -296,7 +296,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -309,6 +309,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat graphify-out/.graphify_python) -c " diff --git a/graphify/skill-droid.md b/graphify/skill-droid.md index 6cde935..8c649da 100644 --- a/graphify/skill-droid.md +++ b/graphify/skill-droid.md @@ -260,7 +260,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -297,7 +297,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -310,6 +310,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-kiro.md b/graphify/skill-kiro.md index b3db443..8109adb 100644 --- a/graphify/skill-kiro.md +++ b/graphify/skill-kiro.md @@ -234,7 +234,7 @@ Process each file one at a time. For each file: - INFERRED: reasonable inference (shared structure, implied dependency) - AMBIGUOUS: uncertain — flag it, do not omit - Code files: semantic edges AST cannot find. Do not re-extract imports. - - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. When adding `calls` edges: source is caller, target is callee. + - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms). Do NOT invent file_types like `concept`. When adding `calls` edges: source is caller, target is callee. - Image files: use vision — understand what the image IS, not just OCR - DEEP_MODE (if --mode deep): be aggressive with INFERRED edges - Semantic similarity: if two concepts solve the same problem without a structural link, add `semantically_similar_to` INFERRED edge (confidence 0.6-0.95). Non-obvious cross-file links only. @@ -243,7 +243,7 @@ Process each file one at a time. For each file: 3. Accumulate results across all files Schema for each file's output: -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} After processing all files, write the accumulated result to `.graphify_semantic_new.json`. @@ -253,6 +253,30 @@ For the accumulated result: If more than half the chunks failed, stop and tell the user. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-opencode.md b/graphify/skill-opencode.md index 32819c8..3f1c005 100644 --- a/graphify/skill-opencode.md +++ b/graphify/skill-opencode.md @@ -261,7 +261,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -298,7 +298,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -311,6 +311,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat graphify-out/.graphify_python) -c " diff --git a/graphify/skill-pi.md b/graphify/skill-pi.md index b3db443..8109adb 100644 --- a/graphify/skill-pi.md +++ b/graphify/skill-pi.md @@ -234,7 +234,7 @@ Process each file one at a time. For each file: - INFERRED: reasonable inference (shared structure, implied dependency) - AMBIGUOUS: uncertain — flag it, do not omit - Code files: semantic edges AST cannot find. Do not re-extract imports. - - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. When adding `calls` edges: source is caller, target is callee. + - Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms). Do NOT invent file_types like `concept`. When adding `calls` edges: source is caller, target is callee. - Image files: use vision — understand what the image IS, not just OCR - DEEP_MODE (if --mode deep): be aggressive with INFERRED edges - Semantic similarity: if two concepts solve the same problem without a structural link, add `semantically_similar_to` INFERRED edge (confidence 0.6-0.95). Non-obvious cross-file links only. @@ -243,7 +243,7 @@ Process each file one at a time. For each file: 3. Accumulate results across all files Schema for each file's output: -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} After processing all files, write the accumulated result to `.graphify_semantic_new.json`. @@ -253,6 +253,30 @@ For the accumulated result: If more than half the chunks failed, stop and tell the user. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-trae.md b/graphify/skill-trae.md index 5f5597e..58ed168 100644 --- a/graphify/skill-trae.md +++ b/graphify/skill-trae.md @@ -250,7 +250,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -287,7 +287,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` After all subagents complete, collect their results. For each result: @@ -306,6 +306,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat .graphify_python) -c " diff --git a/graphify/skill-vscode.md b/graphify/skill-vscode.md index 7d427ac..9e5e3fb 100644 --- a/graphify/skill-vscode.md +++ b/graphify/skill-vscode.md @@ -151,13 +151,16 @@ if cached_path.exists(): all_hyperedges.extend(cached.get('hyperedges', [])) # PASTE each subagent response here as chunk_1, chunk_2, etc. +total_in, total_out = 0, 0 for chunk_json in []: # replace [] with your chunk results chunk = json.loads(chunk_json) if isinstance(chunk_json, str) else chunk_json all_nodes.extend(chunk.get('nodes', [])) all_edges.extend(chunk.get('edges', [])) all_hyperedges.extend(chunk.get('hyperedges', [])) + total_in += chunk.get('input_tokens', 0) + total_out += chunk.get('output_tokens', 0) -merged = {'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, 'input_tokens': 0, 'output_tokens': 0} +merged = {'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, 'input_tokens': total_in, 'output_tokens': total_out} Path('graphify-out/.graphify_extract.json').write_text(json.dumps(merged, indent=2)) print(f'Merged: {len(all_nodes)} nodes, {len(all_edges)} edges') " diff --git a/graphify/skill-windows.md b/graphify/skill-windows.md index cca70b9..f668c2f 100644 --- a/graphify/skill-windows.md +++ b/graphify/skill-windows.md @@ -249,7 +249,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -292,7 +292,7 @@ confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a d - AMBIGUOUS edges: 0.1-0.3 Output exactly this JSON (no other text): -{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -305,6 +305,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```powershell python -c " diff --git a/graphify/skill.md b/graphify/skill.md index c49cbfb..5e4d905 100644 --- a/graphify/skill.md +++ b/graphify/skill.md @@ -307,7 +307,7 @@ Rules: Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). Do not re-extract imports - AST already has those. -Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. +Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). Do NOT invent file_types like `concept` — valid values are only `code|document|paper|image|rationale`. Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. Image files: use vision to understand what the image IS - do not just OCR. UI screenshot: layout patterns, design decisions, key elements, purpose. @@ -352,7 +352,7 @@ 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 the filename without extension and entity is the symbol name, both normalized (lowercase, non-alphanumeric chars replaced with `_`). Example: `src/auth/session.py` + `ValidateToken` → `session_validatetoken`. This must match the ID the AST extractor generates so cross-references between code and semantic nodes connect correctly. 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. Output exactly this JSON (no other text): -{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} +{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale","source_file":"relative/path","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":"relative/path","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":"relative/path"}],"input_tokens":0,"output_tokens":0} ``` **Step B3 - Collect, cache, and merge** @@ -365,6 +365,30 @@ Wait for all subagents. For each result: If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used. +Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run: +```bash +$(cat graphify-out/.graphify_python) -c " +import json, glob +from pathlib import Path + +chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json')) +all_nodes, all_edges, all_hyperedges = [], [], [] +total_in, total_out = 0, 0 +for c in chunks: + d = json.loads(Path(c).read_text()) + all_nodes += d.get('nodes', []) + all_edges += d.get('edges', []) + all_hyperedges += d.get('hyperedges', []) + total_in += d.get('input_tokens', 0) + total_out += d.get('output_tokens', 0) +Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({ + 'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges, + 'input_tokens': total_in, 'output_tokens': total_out, +}, indent=2)) +print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens') +" +``` + Save new results to cache: ```bash $(cat graphify-out/.graphify_python) -c "