fix #655 #656 #657 #658: cache dir crash, sanitize_label None, rationale file_type, token counting

#655 and #656 already fixed in cache.py and security.py.
#657: add rationale to file_type schema in all 12 skill variants; warn against inventing concept.
#658: add explicit chunk-merge step with token summation before save_semantic_cache.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

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 "

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 "

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 "

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 "

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 "

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 "

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 "

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 "

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 "

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')
 "

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 "

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 "