1→--- 2→name: graphify 3→description: any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report 4→trigger: /graphify 5→--- 6→ 7→# /graphify 8→ 9→Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md. 10→ 11→## Usage 12→ 13→``` 14→/graphify # full pipeline on current directory → Obsidian vault 15→/graphify # full pipeline on specific path 16→/graphify --mode deep # thorough extraction, richer INFERRED edges 17→/graphify --update # incremental - re-extract only new/changed files 18→/graphify --cluster-only # rerun clustering on existing graph 19→/graphify --no-viz # skip visualization, just report + JSON 20→/graphify --html # (HTML is generated by default - this flag is a no-op) 21→/graphify --svg # also export graph.svg (embeds in Notion, GitHub) 22→/graphify --graphml # export graph.graphml (Gephi, yEd) 23→/graphify --neo4j # generate graphify-out/cypher.txt for Neo4j 24→/graphify --neo4j-push bolt://localhost:7687 # push directly to Neo4j 25→/graphify --mcp # start MCP stdio server for agent access 26→/graphify --watch # watch folder, auto-rebuild on code changes (no LLM needed) 27→/graphify add # fetch URL, save to ./raw, update graph 28→/graphify add --author "Name" # tag who wrote it 29→/graphify add --contributor "Name" # tag who added it to the corpus 30→/graphify query "" # BFS traversal - broad context 31→/graphify query "" --dfs # DFS - trace a specific path 32→/graphify query "" --budget 1500 # cap answer at N tokens 33→/graphify path "AuthModule" "Database" # shortest path between two concepts 34→/graphify explain "SwinTransformer" # plain-language explanation of a node 35→``` 36→ 37→## What graphify is for 38→ 39→graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected. 40→ 41→Three things it does that Claude alone cannot: 42→1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything. 43→2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented. 44→3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly. 45→ 46→Use it for: 47→- A codebase you're new to (understand architecture before touching anything) 48→- A reading list (papers + tweets + notes → one navigable graph) 49→- A research corpus (citation graph + concept graph in one) 50→- Your personal /raw folder (drop everything in, let it grow, query it) 51→ 52→## What You Must Do When Invoked 53→ 54→If no path was given, use `.` (current directory). Do not ask the user for a path. 55→ 56→Follow these steps in order. Do not skip steps. 57→ 58→### Step 1 - Ensure graphify is installed 59→ 60→```bash 61→python3 -c "import graphify" 2>/dev/null || pip install graphifyy -q --break-system-packages 2>&1 | tail -3 62→``` 63→ 64→If the import succeeds, print nothing and move straight to Step 2. 65→ 66→### Step 2 - Detect files 67→ 68→```bash 69→python3 -c " 70→import json 71→from graphify.detect import detect 72→from pathlib import Path 73→result = detect(Path('INPUT_PATH')) 74→print(json.dumps(result)) 75→" > .graphify_detect.json 76→``` 77→ 78→Replace INPUT_PATH with the actual path the user provided. Do NOT cat or print the JSON - read it silently and present a clean summary instead: 79→ 80→``` 81→Corpus: X files · ~Y words 82→ code: N files (.py .ts .go ...) 83→ docs: N files (.md .txt ...) 84→ papers: N files (.pdf ...) 85→ images: N files 86→``` 87→ 88→Then act on it: 89→- If `total_files` is 0: stop with "No supported files found in [path]." 90→- If `skipped_sensitive` is non-empty: mention file count skipped, not the file names. 91→- If `total_words` > 2,000,000 OR `total_files` > 200: show the warning and the top 5 subdirectories by file count, then ask which subfolder to run on. Wait for the user's answer before proceeding. 92→- Otherwise: proceed directly to Step 3 - no need to ask anything. 93→ 94→### Step 3 - Extract entities and relationships 95→ 96→**Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it. 97→ 98→This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (Claude, costs tokens). 99→ 100→**Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.** 101→ 102→Note: Parallelizing AST + semantic saves 5-15s on large corpora. AST is deterministic and fast; start it while subagents are processing docs/papers. 103→ 104→#### Part A - Structural extraction for code files 105→ 106→For any code files detected, run AST extraction in parallel with Part B subagents: 107→ 108→```bash 109→python3 -c " 110→import sys, json 111→from graphify.extract import collect_files, extract 112→from pathlib import Path 113→import json 114→ 115→code_files = [] 116→detect = json.loads(Path('.graphify_detect.json').read_text()) 117→for f in detect.get('files', {}).get('code', []): 118→ code_files.extend(collect_files(Path(f)) if Path(f).is_dir() else [Path(f)]) 119→ 120→if code_files: 121→ result = extract(code_files) 122→ Path('.graphify_ast.json').write_text(json.dumps(result, indent=2)) 123→ print(f'AST: {len(result[\"nodes\"])} nodes, {len(result[\"edges\"])} edges') 124→else: 125→ Path('.graphify_ast.json').write_text(json.dumps({'nodes':[],'edges':[],'input_tokens':0,'output_tokens':0})) 126→ print('No code files - skipping AST extraction') 127→" 128→``` 129→ 130→#### Part B - Semantic extraction (parallel subagents) 131→ 132→**Fast path:** If detection found zero docs, papers, and images (code-only corpus), skip Part B entirely and go straight to Part C. AST handles code - there is nothing for semantic subagents to do. 133→ 134→**MANDATORY: You MUST use the Agent tool here. Reading files yourself one-by-one is forbidden - it is 5-10x slower. If you do not use the Agent tool you are doing this wrong.** 135→ 136→Before dispatching subagents, print a timing estimate: 137→- Load `total_words` and file counts from `.graphify_detect.json` 138→- Estimate agents needed: `ceil(uncached_non_code_files / 22)` (chunk size is 20-25) 139→- Estimate time: ~45s per agent batch (they run in parallel, so total ≈ 45s × ceil(agents/parallel_limit)) 140→- Print: "Semantic extraction: ~N files → X agents, estimated ~Ys" 141→ 142→**Step B0 - Check extraction cache first** 143→ 144→Before dispatching any subagents, check which files already have cached extraction results: 145→ 146→```bash 147→python3 -c " 148→import json 149→from graphify.cache import check_semantic_cache 150→from pathlib import Path 151→ 152→detect = json.loads(Path('.graphify_detect.json').read_text()) 153→all_files = [f for files in detect['files'].values() for f in files] 154→ 155→cached_nodes, cached_edges, uncached = check_semantic_cache(all_files) 156→ 157→if cached_nodes or cached_edges: 158→ Path('.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges})) 159→Path('.graphify_uncached.txt').write_text('\n'.join(uncached)) 160→print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction') 161→" 162→``` 163→ 164→Only dispatch subagents for files listed in `.graphify_uncached.txt`. If all files are cached, skip to Part C directly. 165→ 166→**Step B1 - Split into chunks** 167→ 168→Load files from `.graphify_uncached.txt`. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). 169→ 170→**Step B2 - Dispatch ALL subagents in a single message** 171→ 172→Call the Agent tool multiple times IN THE SAME RESPONSE - one call per chunk. This is the only way they run in parallel. If you make one Agent call, wait, then make another, you are doing it sequentially and defeating the purpose. 173→ 174→Concrete example for 3 chunks: 175→``` 176→[Agent tool call 1: files 1-15] 177→[Agent tool call 2: files 16-30] 178→[Agent tool call 3: files 31-45] 179→``` 180→All three in one message. Not three separate messages. 181→ 182→Each subagent receives this exact prompt (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, and DEEP_MODE): 183→ 184→``` 185→You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment. 186→Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble. 187→ 188→Files (chunk CHUNK_NUM of TOTAL_CHUNKS): 189→FILE_LIST 190→ 191→Rules: 192→- EXTRACTED: relationship explicit in source (import, call, citation, "see §3.2") 193→- INFERRED: reasonable inference (shared data structure, implied dependency) 194→- AMBIGUOUS: uncertain - flag for review, do not omit 195→ 196→Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns). 197→ Do not re-extract imports - AST already has those. 198→Doc/paper files: extract named concepts, entities, citations. 199→Image files: use vision to understand what the image IS - do not just OCR. 200→ UI screenshot: layout patterns, design decisions, key elements, purpose. 201→ Chart: metric, trend/insight, data source. 202→ Tweet/post: claim as node, author, concepts mentioned. 203→ Diagram: components and connections. 204→ Research figure: what it demonstrates, method, result. 205→ Handwritten/whiteboard: ideas and arrows, mark uncertain readings AMBIGUOUS. 206→ 207→DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps, 208→ shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting. 209→ 210→Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples: 211→- Two functions that both validate user input but never call each other 212→- A class in code and a concept in a paper that describe the same algorithm 213→- Two error types that handle the same failure mode differently 214→Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things. 215→ 216→Hyperedges: if 3 or more nodes clearly participate together in a shared concept, flow, or pattern that is not captured by pairwise edges alone, add a hyperedge to a top-level `hyperedges` array. Examples: 217→- All classes that implement a common protocol or interface 218→- All functions in an authentication flow (even if they don't all call each other) 219→- All concepts from a paper section that form one coherent idea 220→Use sparingly — only when the group relationship adds information beyond the pairwise edges. Maximum 3 hyperedges per chunk. 221→ 222→If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author, 223→ contributor onto every node from that file. 224→ 225→confidence_score rules: 226→- EXTRACTED edges: confidence_score must be 1.0 227→- INFERRED edges: score 0.4-0.9 based on how certain you are. 228→ Strong structural inference (e.g. two classes clearly share data): 0.8-0.9. 229→ Reasonable but not certain: 0.6-0.7. Weak inference: 0.4-0.5. 230→- AMBIGUOUS edges: score 0.1-0.3 231→ 232→Output exactly this JSON (no other text): 233→{"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","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} 234→``` 235→ 236→**Step B3 - Collect, cache, and merge** 237→ 238→Wait for all subagents. For each result: 239→- If a subagent returned valid JSON with `nodes` and `edges`, include it and save each file's nodes/edges to the cache 240→- If a subagent failed or returned invalid JSON, print a warning and skip that chunk - do not abort 241→ 242→If more than half the chunks failed, stop and tell the user. 243→ 244→Save new results to cache: 245→```bash 246→python3 -c " 247→import json 248→from graphify.cache import save_semantic_cache 249→from pathlib import Path 250→ 251→new = json.loads(Path('.graphify_semantic_new.json').read_text()) if Path('.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[]} 252→saved = save_semantic_cache(new.get('nodes', []), new.get('edges', [])) 253→print(f'Cached {saved} files') 254→" 255→``` 256→ 257→Merge cached + new results into `.graphify_semantic.json`: 258→```bash 259→python3 -c " 260→import json 261→from pathlib import Path 262→ 263→cached = json.loads(Path('.graphify_cached.json').read_text()) if Path('.graphify_cached.json').exists() else {'nodes':[],'edges':[]} 264→new = json.loads(Path('.graphify_semantic_new.json').read_text()) if Path('.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[]} 265→ 266→all_nodes = cached['nodes'] + new.get('nodes', []) 267→all_edges = cached['edges'] + new.get('edges', []) 268→seen = set() 269→deduped = [] 270→for n in all_nodes: 271→ if n['id'] not in seen: 272→ seen.add(n['id']) 273→ deduped.append(n) 274→ 275→merged = { 276→ 'nodes': deduped, 277→ 'edges': all_edges, 278→ 'input_tokens': new.get('input_tokens', 0), 279→ 'output_tokens': new.get('output_tokens', 0), 280→} 281→Path('.graphify_semantic.json').write_text(json.dumps(merged, indent=2)) 282→print(f'Extraction complete - {len(deduped)} nodes, {len(all_edges)} edges ({len(cached[\"nodes\"])} from cache, {len(new.get(\"nodes\",[]))} new)') 283→" 284→``` 285→Clean up temp files: `rm -f .graphify_cached.json .graphify_uncached.txt .graphify_semantic_new.json` 286→ 287→#### Part C - Merge AST + semantic into final extraction 288→ 289→```bash 290→python3 -c " 291→import sys, json 292→from pathlib import Path 293→ 294→ast = json.loads(Path('.graphify_ast.json').read_text()) 295→sem = json.loads(Path('.graphify_semantic.json').read_text()) 296→ 297→# Merge: AST nodes first, semantic nodes deduplicated by id 298→seen = {n['id'] for n in ast['nodes']} 299→merged_nodes = list(ast['nodes']) 300→for n in sem['nodes']: 301→ if n['id'] not in seen: 302→ merged_nodes.append(n) 303→ seen.add(n['id']) 304→ 305→merged_edges = ast['edges'] + sem['edges'] 306→merged = { 307→ 'nodes': merged_nodes, 308→ 'edges': merged_edges, 309→ 'input_tokens': sem.get('input_tokens', 0), 310→ 'output_tokens': sem.get('output_tokens', 0), 311→} 312→Path('.graphify_extract.json').write_text(json.dumps(merged, indent=2)) 313→total = len(merged_nodes) 314→edges = len(merged_edges) 315→print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(sem[\"nodes\"])} semantic)') 316→" 317→``` 318→ 319→### Step 4 - Build graph, cluster, analyze, generate outputs 320→ 321→```bash 322→mkdir -p graphify-out 323→python3 -c " 324→import sys, json 325→from graphify.build import build_from_json 326→from graphify.cluster import cluster, score_all 327→from graphify.analyze import god_nodes, surprising_connections, suggest_questions 328→from graphify.report import generate 329→from graphify.export import to_json 330→from pathlib import Path 331→ 332→extraction = json.loads(Path('.graphify_extract.json').read_text()) 333→detection = json.loads(Path('.graphify_detect.json').read_text()) 334→ 335→G = build_from_json(extraction) 336→communities = cluster(G) 337→cohesion = score_all(G, communities) 338→tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)} 339→gods = god_nodes(G) 340→surprises = surprising_connections(G, communities) 341→labels = {cid: 'Community ' + str(cid) for cid in communities} 342→# Placeholder questions - regenerated with real labels in Step 5 343→questions = suggest_questions(G, communities, labels) 344→ 345→report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, 'INPUT_PATH', suggested_questions=questions) 346→Path('graphify-out/GRAPH_REPORT.md').write_text(report) 347→to_json(G, communities, 'graphify-out/graph.json') 348→ 349→analysis = { 350→ 'communities': {str(k): v for k, v in communities.items()}, 351→ 'cohesion': {str(k): v for k, v in cohesion.items()}, 352→ 'gods': gods, 353→ 'surprises': surprises, 354→ 'questions': questions, 355→} 356→Path('.graphify_analysis.json').write_text(json.dumps(analysis, indent=2)) 357→if G.number_of_nodes() == 0: 358→ print('ERROR: Graph is empty - extraction produced no nodes.') 359→ print('Possible causes: all files were skipped, binary-only corpus, or extraction failed.') 360→ raise SystemExit(1) 361→print(f'Graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges, {len(communities)} communities') 362→" 363→``` 364→ 365→If this step prints `ERROR: Graph is empty`, stop and tell the user what happened - do not proceed to labeling or visualization. 366→ 367→Replace INPUT_PATH with the actual path. 368→ 369→### Step 5 - Label communities 370→ 371→Read `.graphify_analysis.json`. For each community key, look at its node labels and write a 2-5 word plain-language name (e.g. "Attention Mechanism", "Training Pipeline", "Data Loading"). 372→ 373→Then regenerate the report and save the labels for the visualizer: 374→ 375→```bash 376→python3 -c " 377→import sys, json 378→from graphify.build import build_from_json 379→from graphify.cluster import score_all 380→from graphify.analyze import god_nodes, surprising_connections, suggest_questions 381→from graphify.report import generate 382→from pathlib import Path 383→ 384→extraction = json.loads(Path('.graphify_extract.json').read_text()) 385→detection = json.loads(Path('.graphify_detect.json').read_text()) 386→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 387→ 388→G = build_from_json(extraction) 389→communities = {int(k): v for k, v in analysis['communities'].items()} 390→cohesion = {int(k): v for k, v in analysis['cohesion'].items()} 391→tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)} 392→ 393→# LABELS - replace these with the names you chose above 394→labels = LABELS_DICT 395→ 396→# Regenerate questions with real community labels (labels affect question phrasing) 397→questions = suggest_questions(G, communities, labels) 398→ 399→report = generate(G, communities, cohesion, labels, analysis['gods'], analysis['surprises'], detection, tokens, 'INPUT_PATH', suggested_questions=questions) 400→Path('graphify-out/GRAPH_REPORT.md').write_text(report) 401→Path('.graphify_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()})) 402→print('Report updated with community labels') 403→" 404→``` 405→ 406→Replace `LABELS_DICT` with the actual dict you constructed (e.g. `{0: "Attention Mechanism", 1: "Training Pipeline"}`). 407→Replace INPUT_PATH with the actual path. 408→ 409→### Step 6 - Generate Obsidian vault (default) + optional HTML 410→ 411→**Always generate the Obsidian vault and HTML** - they are the primary visualizations. Skip both if `--no-viz` (report + JSON only). 412→ 413→```bash 414→python3 -c " 415→import sys, json 416→from graphify.build import build_from_json 417→from graphify.export import to_obsidian, to_canvas 418→from pathlib import Path 419→ 420→extraction = json.loads(Path('.graphify_extract.json').read_text()) 421→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 422→labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {} 423→ 424→G = build_from_json(extraction) 425→communities = {int(k): v for k, v in analysis['communities'].items()} 426→cohesion = {int(k): v for k, v in analysis['cohesion'].items()} 427→labels = {int(k): v for k, v in labels_raw.items()} 428→ 429→n = to_obsidian(G, communities, 'graphify-out/obsidian', community_labels=labels or None, cohesion=cohesion) 430→print(f'Obsidian vault: {n} notes in graphify-out/obsidian/') 431→ 432→to_canvas(G, communities, 'graphify-out/obsidian/graph.canvas', community_labels=labels or None) 433→print('Canvas: graphify-out/obsidian/graph.canvas - open in Obsidian for structured community layout') 434→print() 435→print('Open graphify-out/obsidian/ as a vault in Obsidian.') 436→print(' Graph view - nodes colored by community (set automatically)') 437→print(' graph.canvas - structured layout with communities as groups') 438→print(' _COMMUNITY_* - overview notes with cohesion scores and dataview queries') 439→" 440→``` 441→ 442→Also generate the HTML graph (always, unless `--no-viz`): 443→ 444→```bash 445→python3 -c " 446→import sys, json 447→from graphify.build import build_from_json 448→from graphify.export import to_html 449→from pathlib import Path 450→ 451→extraction = json.loads(Path('.graphify_extract.json').read_text()) 452→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 453→labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {} 454→ 455→G = build_from_json(extraction) 456→communities = {int(k): v for k, v in analysis['communities'].items()} 457→labels = {int(k): v for k, v in labels_raw.items()} 458→ 459→if G.number_of_nodes() > 5000: 460→ print(f'Graph has {G.number_of_nodes()} nodes - too large for HTML viz. Use Obsidian vault instead.') 461→else: 462→ to_html(G, communities, 'graphify-out/graph.html', community_labels=labels or None) 463→ print('graph.html written - open in any browser, no server needed') 464→" 465→``` 466→ 467→### Step 7 - Neo4j export (only if --neo4j or --neo4j-push flag) 468→ 469→**If `--neo4j`** - generate a Cypher file for manual import: 470→ 471→```bash 472→python3 -c " 473→import sys, json 474→from graphify.build import build_from_json 475→from graphify.export import to_cypher 476→from pathlib import Path 477→ 478→G = build_from_json(json.loads(Path('.graphify_extract.json').read_text())) 479→to_cypher(G, 'graphify-out/cypher.txt') 480→print('cypher.txt written - import with: cypher-shell < graphify-out/cypher.txt') 481→" 482→``` 483→ 484→**If `--neo4j-push `** - push directly to a running Neo4j instance. Ask the user for credentials if not provided: 485→ 486→```bash 487→python3 -c " 488→import sys, json 489→from graphify.build import build_from_json 490→from graphify.cluster import cluster 491→from graphify.export import push_to_neo4j 492→from pathlib import Path 493→ 494→extraction = json.loads(Path('.graphify_extract.json').read_text()) 495→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 496→G = build_from_json(extraction) 497→communities = {int(k): v for k, v in analysis['communities'].items()} 498→ 499→result = push_to_neo4j(G, uri='NEO4J_URI', user='NEO4J_USER', password='NEO4J_PASSWORD', communities=communities) 500→print(f'Pushed to Neo4j: {result[\"nodes\"]} nodes, {result[\"edges\"]} edges') 501→" 502→``` 503→ 504→Replace `NEO4J_URI`, `NEO4J_USER`, `NEO4J_PASSWORD` with actual values. Default URI is `bolt://localhost:7687`, default user is `neo4j`. Uses MERGE - safe to re-run without creating duplicates. 505→ 506→### Step 7b - SVG export (only if --svg flag) 507→ 508→```bash 509→python3 -c " 510→import sys, json 511→from graphify.build import build_from_json 512→from graphify.export import to_svg 513→from pathlib import Path 514→ 515→extraction = json.loads(Path('.graphify_extract.json').read_text()) 516→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 517→labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {} 518→ 519→G = build_from_json(extraction) 520→communities = {int(k): v for k, v in analysis['communities'].items()} 521→labels = {int(k): v for k, v in labels_raw.items()} 522→ 523→to_svg(G, communities, 'graphify-out/graph.svg', community_labels=labels or None) 524→print('graph.svg written - embeds in Obsidian, Notion, GitHub READMEs') 525→" 526→``` 527→ 528→### Step 7c - GraphML export (only if --graphml flag) 529→ 530→```bash 531→python3 -c " 532→import json 533→from graphify.build import build_from_json 534→from graphify.export import to_graphml 535→from pathlib import Path 536→ 537→extraction = json.loads(Path('.graphify_extract.json').read_text()) 538→analysis = json.loads(Path('.graphify_analysis.json').read_text()) 539→ 540→G = build_from_json(extraction) 541→communities = {int(k): v for k, v in analysis['communities'].items()} 542→ 543→to_graphml(G, communities, 'graphify-out/graph.graphml') 544→print('graph.graphml written - open in Gephi, yEd, or any GraphML tool') 545→" 546→``` 547→ 548→### Step 7d - MCP server (only if --mcp flag) 549→ 550→```bash 551→python3 -m graphify.serve graphify-out/graph.json 552→``` 553→ 554→This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live. 555→ 556→To configure in Claude Desktop, add to `claude_desktop_config.json`: 557→```json 558→{ 559→ "mcpServers": { 560→ "graphify": { 561→ "command": "python3", 562→ "args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"] 563→ } 564→ } 565→} 566→``` 567→ 568→### Step 8 - Token reduction benchmark (only if total_words > 5000) 569→ 570→If `total_words` from `.graphify_detect.json` is greater than 5,000, run: 571→ 572→```bash 573→python3 -c " 574→import json 575→from graphify.benchmark import run_benchmark, print_benchmark 576→from pathlib import Path 577→ 578→detection = json.loads(Path('.graphify_detect.json').read_text()) 579→result = run_benchmark('graphify-out/graph.json', corpus_words=detection['total_words']) 580→print_benchmark(result) 581→" 582→``` 583→ 584→Print the output directly in chat. If `total_words <= 5000`, skip silently - the graph value is structural clarity, not token compression, for small corpora. 585→ 586→--- 587→ 588→### Step 9 - Save manifest, update cost tracker, clean up, and report 589→ 590→```bash 591→python3 -c " 592→import json 593→from pathlib import Path 594→from datetime import datetime, timezone 595→from graphify.detect import save_manifest 596→ 597→# Save manifest for --update 598→detect = json.loads(Path('.graphify_detect.json').read_text()) 599→save_manifest(detect['files']) 600→ 601→# Update cumulative cost tracker 602→extract = json.loads(Path('.graphify_extract.json').read_text()) 603→input_tok = extract.get('input_tokens', 0) 604→output_tok = extract.get('output_tokens', 0) 605→ 606→cost_path = Path('graphify-out/cost.json') 607→if cost_path.exists(): 608→ cost = json.loads(cost_path.read_text()) 609→else: 610→ cost = {'runs': [], 'total_input_tokens': 0, 'total_output_tokens': 0} 611→ 612→cost['runs'].append({ 613→ 'date': datetime.now(timezone.utc).isoformat(), 614→ 'input_tokens': input_tok, 615→ 'output_tokens': output_tok, 616→ 'files': detect.get('total_files', 0), 617→}) 618→cost['total_input_tokens'] += input_tok 619→cost['total_output_tokens'] += output_tok 620→cost_path.write_text(json.dumps(cost, indent=2)) 621→ 622→print(f'This run: {input_tok:,} input tokens, {output_tok:,} output tokens') 623→print(f'All time: {cost[\"total_input_tokens\"]:,} input, {cost[\"total_output_tokens\"]:,} output ({len(cost[\"runs\"])} runs)') 624→" 625→rm -f .graphify_detect.json .graphify_extract.json .graphify_ast.json .graphify_semantic.json .graphify_analysis.json .graphify_labels.json 626→rm -f graphify-out/.needs_update 2>/dev/null || true 627→``` 628→ 629→Tell the user: 630→``` 631→Graph complete. Outputs are in a hidden folder called graphify-out/ inside the directory you ran this on. 632→ 633→The folder is hidden (dot prefix) so it won't show in Finder or a normal ls. 634→To see it: 635→ Mac/Linux: ls -la graphify-out/ 636→ VS Code: the Explorer panel shows hidden files by default 637→ Finder: Cmd+Shift+. to toggle hidden files 638→ 639→What's inside: 640→ graphify-out/obsidian/ - open this folder as a vault in Obsidian (File > Open Vault) 641→ graphify-out/GRAPH_REPORT.md - full audit report, also readable here in Claude 642→ graphify-out/graph.json - persistent graph, query it later with /graphify query "..." 643→ 644→Full path: PATH_TO_DIR/graphify-out/ 645→``` 646→ 647→Replace PATH_TO_DIR with the actual absolute path of the directory that was processed. 648→ 649→Then paste these sections from GRAPH_REPORT.md directly into the chat: 650→- God Nodes 651→- Surprising Connections 652→- Suggested Questions 653→ 654→Do NOT paste the full report - just those three sections. Keep it concise. 655→ 656→Then immediately offer to explore. Pick the single most interesting suggested question from the report - the one that crosses the most community boundaries or has the most surprising bridge node - and ask: 657→ 658→> "The most interesting question this graph can answer: **[question]**. Want me to trace it?" 659→ 660→If the user says yes, run `/graphify query "[question]"` on the graph and walk them through the answer using the graph structure - which nodes connect, which community boundaries get crossed, what the path reveals. Keep going as long as they want to explore. Each answer should end with a natural follow-up ("this connects to X - want to go deeper?") so the session feels like navigation, not a one-shot report. 661→ 662→The graph is the map. Your job after the pipeline is to be the guide. 663→ 664→--- 665→ 666→## For --update (incremental re-extraction) 667→ 668→Use when you've added or modified files since the last run. Only re-extracts changed files - saves tokens and time. 669→ 670→```bash 671→python3 -c " 672→import sys, json 673→from graphify.detect import detect_incremental, save_manifest 674→from pathlib import Path 675→ 676→result = detect_incremental(Path('INPUT_PATH')) 677→new_total = result.get('new_total', 0) 678→print(json.dumps(result, indent=2)) 679→Path('.graphify_incremental.json').write_text(json.dumps(result)) 680→if new_total == 0: 681→ print('No files changed since last run. Nothing to update.') 682→ raise SystemExit(0) 683→print(f'{new_total} new/changed file(s) to re-extract.') 684→" 685→``` 686→ 687→If new files exist, first check whether all changed files are code files: 688→ 689→```bash 690→python3 -c " 691→import json 692→from pathlib import Path 693→ 694→result = json.loads(open('.graphify_incremental.json').read()) if Path('.graphify_incremental.json').exists() else {} 695→code_exts = {'.py','.ts','.js','.go','.rs','.java','.cpp','.c','.rb','.swift','.kt','.cs','.scala','.php','.cc','.cxx','.hpp','.h','.kts'} 696→new_files = result.get('new_files', {}) 697→all_changed = [f for files in new_files.values() for f in files] 698→code_only = all(Path(f).suffix.lower() in code_exts for f in all_changed) 699→print('code_only:', code_only) 700→" 701→``` 702→ 703→If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8. 704→ 705→If `code_only` is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal. 706→ 707→Then: 708→ 709→```bash 710→python3 -c " 711→import sys, json 712→from graphify.build import build_from_json 713→from graphify.export import to_json 714→from networkx.readwrite import json_graph 715→import networkx as nx 716→from pathlib import Path 717→ 718→# Load existing graph 719→existing_data = json.loads(Path('graphify-out/graph.json').read_text()) 720→G_existing = json_graph.node_link_graph(existing_data, edges='links') 721→ 722→# Load new extraction 723→new_extraction = json.loads(Path('.graphify_extract.json').read_text()) 724→G_new = build_from_json(new_extraction) 725→ 726→# Merge: new nodes/edges into existing graph 727→G_existing.update(G_new) 728→print(f'Merged: {G_existing.number_of_nodes()} nodes, {G_existing.number_of_edges()} edges') 729→" 730→``` 731→ 732→Then run Steps 4–8 on the merged graph as normal. 733→ 734→After Step 4, show the graph diff: 735→ 736→```bash 737→python3 -c " 738→import json 739→from graphify.analyze import graph_diff 740→from graphify.build import build_from_json 741→from networkx.readwrite import json_graph 742→import networkx as nx 743→from pathlib import Path 744→ 745→# Load old graph (before update) from backup written before merge 746→old_data = json.loads(Path('.graphify_old.json').read_text()) if Path('.graphify_old.json').exists() else None 747→new_extract = json.loads(Path('.graphify_extract.json').read_text()) 748→G_new = build_from_json(new_extract) 749→ 750→if old_data: 751→ G_old = json_graph.node_link_graph(old_data, edges='links') 752→ diff = graph_diff(G_old, G_new) 753→ print(diff['summary']) 754→ if diff['new_nodes']: 755→ print('New nodes:', ', '.join(n['label'] for n in diff['new_nodes'][:5])) 756→ if diff['new_edges']: 757→ print('New edges:', len(diff['new_edges'])) 758→" 759→``` 760→ 761→Before the merge step, save the old graph: `cp graphify-out/graph.json .graphify_old.json` 762→Clean up after: `rm -f .graphify_old.json` 763→ 764→--- 765→ 766→## For --cluster-only 767→ 768→Skip Steps 1–3. Load the existing graph from `graphify-out/graph.json` and re-run clustering: 769→ 770→```bash 771→python3 -c " 772→import sys, json 773→from graphify.cluster import cluster, score_all 774→from graphify.analyze import god_nodes, surprising_connections 775→from graphify.report import generate 776→from graphify.export import to_json 777→from networkx.readwrite import json_graph 778→import networkx as nx 779→from pathlib import Path 780→ 781→data = json.loads(Path('graphify-out/graph.json').read_text()) 782→G = json_graph.node_link_graph(data, edges='links') 783→ 784→detection = {'total_files': 0, 'total_words': 99999, 'needs_graph': True, 'warning': None, 785→ 'files': {'code': [], 'document': [], 'paper': []}} 786→tokens = {'input': 0, 'output': 0} 787→ 788→communities = cluster(G) 789→cohesion = score_all(G, communities) 790→gods = god_nodes(G) 791→surprises = surprising_connections(G, communities) 792→labels = {cid: 'Community ' + str(cid) for cid in communities} 793→ 794→report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, '.') 795→Path('graphify-out/GRAPH_REPORT.md').write_text(report) 796→to_json(G, communities, 'graphify-out/graph.json') 797→ 798→analysis = { 799→ 'communities': {str(k): v for k, v in communities.items()}, 800→ 'cohesion': {str(k): v for k, v in cohesion.items()}, 801→ 'gods': gods, 802→ 'surprises': surprises, 803→} 804→Path('.graphify_analysis.json').write_text(json.dumps(analysis, indent=2)) 805→print(f'Re-clustered: {len(communities)} communities') 806→" 807→``` 808→ 809→Then run Steps 5–9 as normal (label communities, generate viz, benchmark, clean up, report). 810→ 811→--- 812→ 813→## For /graphify query 814→ 815→Two traversal modes - choose based on the question: 816→ 817→| Mode | Flag | Best for | 818→|------|------|----------| 819→| BFS (default) | _(none)_ | "What is X connected to?" - broad context, nearest neighbors first | 820→| DFS | `--dfs` | "How does X reach Y?" - trace a specific chain or dependency path | 821→ 822→First check the graph exists: 823→```bash 824→python3 -c " 825→from pathlib import Path 826→if not Path('graphify-out/graph.json').exists(): 827→ print('ERROR: No graph found. Run /graphify first to build the graph.') 828→ raise SystemExit(1) 829→" 830→``` 831→If it fails, stop and tell the user to run `/graphify ` first. 832→ 833→Load `graphify-out/graph.json`, then: 834→ 835→1. Find the 1-3 nodes whose label best matches key terms in the question. 836→2. Run the appropriate traversal from each starting node. 837→3. Read the subgraph - node labels, edge relations, confidence tags, source locations. 838→4. Answer using **only** what the graph contains. Quote `source_location` when citing a specific fact. 839→5. If the graph lacks enough information, say so - do not hallucinate edges. 840→ 841→```bash 842→python3 -c " 843→import sys, json 844→from networkx.readwrite import json_graph 845→import networkx as nx 846→from pathlib import Path 847→ 848→data = json.loads(Path('graphify-out/graph.json').read_text()) 849→G = json_graph.node_link_graph(data, edges='links') 850→ 851→question = 'QUESTION' 852→mode = 'MODE' # 'bfs' or 'dfs' 853→terms = [t.lower() for t in question.split() if len(t) > 3] 854→ 855→# Find best-matching start nodes 856→scored = [] 857→for nid, ndata in G.nodes(data=True): 858→ label = ndata.get('label', '').lower() 859→ score = sum(1 for t in terms if t in label) 860→ if score > 0: 861→ scored.append((score, nid)) 862→scored.sort(reverse=True) 863→start_nodes = [nid for _, nid in scored[:3]] 864→ 865→if not start_nodes: 866→ print('No matching nodes found for query terms:', terms) 867→ sys.exit(0) 868→ 869→subgraph_nodes = set() 870→subgraph_edges = [] 871→ 872→if mode == 'dfs': 873→ # DFS: follow one path as deep as possible before backtracking. 874→ # Depth-limited to 6 to avoid traversing the whole graph. 875→ visited = set() 876→ stack = [(n, 0) for n in reversed(start_nodes)] 877→ while stack: 878→ node, depth = stack.pop() 879→ if node in visited or depth > 6: 880→ continue 881→ visited.add(node) 882→ subgraph_nodes.add(node) 883→ for neighbor in G.neighbors(node): 884→ if neighbor not in visited: 885→ stack.append((neighbor, depth + 1)) 886→ subgraph_edges.append((node, neighbor)) 887→else: 888→ # BFS: explore all neighbors layer by layer up to depth 3. 889→ frontier = set(start_nodes) 890→ subgraph_nodes = set(start_nodes) 891→ for _ in range(3): 892→ next_frontier = set() 893→ for n in frontier: 894→ for neighbor in G.neighbors(n): 895→ if neighbor not in subgraph_nodes: 896→ next_frontier.add(neighbor) 897→ subgraph_edges.append((n, neighbor)) 898→ subgraph_nodes.update(next_frontier) 899→ frontier = next_frontier 900→ 901→# Token-budget aware output: rank by relevance, cut at budget (~4 chars/token) 902→token_budget = BUDGET # default 2000 903→char_budget = token_budget * 4 904→ 905→# Score each node by term overlap for ranked output 906→def relevance(nid): 907→ label = G.nodes[nid].get('label', '').lower() 908→ return sum(1 for t in terms if t in label) 909→ 910→ranked_nodes = sorted(subgraph_nodes, key=relevance, reverse=True) 911→ 912→lines = [f'Traversal: {mode.upper()} | Start: {[G.nodes[n].get(\"label\",n) for n in start_nodes]} | {len(subgraph_nodes)} nodes'] 913→for nid in ranked_nodes: 914→ d = G.nodes[nid] 915→ lines.append(f' NODE {d.get(\"label\", nid)} [src={d.get(\"source_file\",\"\")} loc={d.get(\"source_location\",\"\")}]') 916→for u, v in subgraph_edges: 917→ if u in subgraph_nodes and v in subgraph_nodes: 918→ d = G.edges[u, v] 919→ lines.append(f' EDGE {G.nodes[u].get(\"label\",u)} --{d.get(\"relation\",\"\")} [{d.get(\"confidence\",\"\")}]--> {G.nodes[v].get(\"label\",v)}') 920→ 921→output = '\n'.join(lines) 922→if len(output) > char_budget: 923→ output = output[:char_budget] + f'\n... (truncated at ~{token_budget} token budget - use --budget N for more)' 924→print(output) 925→" 926→``` 927→ 928→Replace `QUESTION` with the user's actual question, `MODE` with `bfs` or `dfs`, and `BUDGET` with the token budget (default `2000`, or whatever `--budget N` specifies). Then answer based on the subgraph output above. 929→ 930→After writing the answer, save it back into the graph so it improves future queries: 931→ 932→```bash 933→python3 -c " 934→from graphify.ingest import save_query_result 935→from pathlib import Path 936→save_query_result( 937→ question='QUESTION', 938→ answer='ANSWER', 939→ memory_dir=Path('graphify-out/memory'), 940→ query_type='query', 941→ source_nodes=SOURCE_NODES, # list of node labels cited, or [] 942→) 943→print('Query result saved to graphify-out/memory/') 944→" 945→``` 946→ 947→Replace `QUESTION` with the question, `ANSWER` with your full answer text, `SOURCE_NODES` with the list of node labels you cited. This closes the feedback loop: the next `--update` will extract this Q&A as a node in the graph. 948→ 949→--- 950→ 951→## For /graphify path 952→ 953→Find the shortest path between two named concepts in the graph. 954→ 955→First check the graph exists: 956→```bash 957→python3 -c " 958→from pathlib import Path 959→if not Path('graphify-out/graph.json').exists(): 960→ print('ERROR: No graph found. Run /graphify first to build the graph.') 961→ raise SystemExit(1) 962→" 963→``` 964→If it fails, stop and tell the user to run `/graphify ` first. 965→ 966→```bash 967→python3 -c " 968→import json, sys 969→import networkx as nx 970→from networkx.readwrite import json_graph 971→from pathlib import Path 972→ 973→data = json.loads(Path('graphify-out/graph.json').read_text()) 974→G = json_graph.node_link_graph(data, edges='links') 975→ 976→a_term = 'NODE_A' 977→b_term = 'NODE_B' 978→ 979→def find_node(term): 980→ term = term.lower() 981→ scored = sorted( 982→ [(sum(1 for w in term.split() if w in G.nodes[n].get('label','').lower()), n) 983→ for n in G.nodes()], 984→ reverse=True 985→ ) 986→ return scored[0][1] if scored and scored[0][0] > 0 else None 987→ 988→src = find_node(a_term) 989→tgt = find_node(b_term) 990→ 991→if not src or not tgt: 992→ print(f'Could not find nodes matching: {a_term!r} or {b_term!r}') 993→ sys.exit(0) 994→ 995→try: 996→ path = nx.shortest_path(G, src, tgt) 997→ print(f'Shortest path ({len(path)-1} hops):') 998→ for i, nid in enumerate(path): 999→ label = G.nodes[nid].get('label', nid) 1000→ if i < len(path) - 1: 1001→ edge = G.edges[nid, path[i+1]] 1002→ rel = edge.get('relation', '') 1003→ conf = edge.get('confidence', '') 1004→ print(f' {label} --{rel}--> [{conf}]') 1005→ else: 1006→ print(f' {label}') 1007→except nx.NetworkXNoPath: 1008→ print(f'No path found between {a_term!r} and {b_term!r}') 1009→except nx.NodeNotFound as e: 1010→ print(f'Node not found: {e}') 1011→" 1012→``` 1013→ 1014→Replace `NODE_A` and `NODE_B` with the actual concept names from the user. Then explain the path in plain language - what each hop means, why it's significant. 1015→ 1016→After writing the explanation, save it back: 1017→ 1018→```bash 1019→python3 -c " 1020→from graphify.ingest import save_query_result 1021→from pathlib import Path 1022→save_query_result( 1023→ question='Path from NODE_A to NODE_B', 1024→ answer='ANSWER', 1025→ memory_dir=Path('graphify-out/memory'), 1026→ query_type='path_query', 1027→ source_nodes=PATH_NODES, # list of node labels on the path 1028→) 1029→print('Path result saved to graphify-out/memory/') 1030→" 1031→``` 1032→ 1033→--- 1034→ 1035→## For /graphify explain 1036→ 1037→Give a plain-language explanation of a single node - everything connected to it. 1038→ 1039→First check the graph exists: 1040→```bash 1041→python3 -c " 1042→from pathlib import Path 1043→if not Path('graphify-out/graph.json').exists(): 1044→ print('ERROR: No graph found. Run /graphify first to build the graph.') 1045→ raise SystemExit(1) 1046→" 1047→``` 1048→If it fails, stop and tell the user to run `/graphify ` first. 1049→ 1050→```bash 1051→python3 -c " 1052→import json, sys 1053→import networkx as nx 1054→from networkx.readwrite import json_graph 1055→from pathlib import Path 1056→ 1057→data = json.loads(Path('graphify-out/graph.json').read_text()) 1058→G = json_graph.node_link_graph(data, edges='links') 1059→ 1060→term = 'NODE_NAME' 1061→term_lower = term.lower() 1062→ 1063→# Find best matching node 1064→scored = sorted( 1065→ [(sum(1 for w in term_lower.split() if w in G.nodes[n].get('label','').lower()), n) 1066→ for n in G.nodes()], 1067→ reverse=True 1068→) 1069→if not scored or scored[0][0] == 0: 1070→ print(f'No node matching {term!r}') 1071→ sys.exit(0) 1072→ 1073→nid = scored[0][1] 1074→data_n = G.nodes[nid] 1075→print(f'NODE: {data_n.get(\"label\", nid)}') 1076→print(f' source: {data_n.get(\"source_file\",\"unknown\")}') 1077→print(f' type: {data_n.get(\"file_type\",\"unknown\")}') 1078→print(f' degree: {G.degree(nid)}') 1079→print() 1080→print('CONNECTIONS:') 1081→for neighbor in G.neighbors(nid): 1082→ edge = G.edges[nid, neighbor] 1083→ nlabel = G.nodes[neighbor].get('label', neighbor) 1084→ rel = edge.get('relation', '') 1085→ conf = edge.get('confidence', '') 1086→ src_file = G.nodes[neighbor].get('source_file', '') 1087→ print(f' --{rel}--> {nlabel} [{conf}] ({src_file})') 1088→" 1089→``` 1090→ 1091→Replace `NODE_NAME` with the concept the user asked about. Then write a 3-5 sentence explanation of what this node is, what it connects to, and why those connections are significant. Use the source locations as citations. 1092→ 1093→After writing the explanation, save it back: 1094→ 1095→```bash 1096→python3 -c " 1097→from graphify.ingest import save_query_result 1098→from pathlib import Path 1099→save_query_result( 1100→ question='Explain NODE_NAME', 1101→ answer='ANSWER', 1102→ memory_dir=Path('graphify-out/memory'), 1103→ query_type='explain', 1104→ source_nodes=['NODE_NAME'], 1105→) 1106→print('Explanation saved to graphify-out/memory/') 1107→" 1108→``` 1109→ 1110→--- 1111→ 1112→## For /graphify add 1113→ 1114→Fetch a URL and add it to the corpus, then update the graph. 1115→ 1116→```bash 1117→python3 -c " 1118→import sys 1119→from graphify.ingest import ingest 1120→from pathlib import Path 1121→ 1122→try: 1123→ out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR') 1124→ print(f'Saved to {out}') 1125→except ValueError as e: 1126→ print(f'error: {e}', file=sys.stderr) 1127→ sys.exit(1) 1128→except RuntimeError as e: 1129→ print(f'error: {e}', file=sys.stderr) 1130→ sys.exit(1) 1131→" 1132→``` 1133→ 1134→Replace `URL` with the actual URL, `AUTHOR` with the user's name if provided, `CONTRIBUTOR` likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the `--update` pipeline on `./raw` to merge the new file into the existing graph. 1135→ 1136→Supported URL types (auto-detected): 1137→- Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author 1138→- arXiv → abstract + metadata saved as `.md` 1139→- PDF → downloaded as `.pdf` 1140→- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run 1141→- Any webpage → converted to markdown via html2text 1142→ 1143→--- 1144→ 1145→## For --watch 1146→ 1147→Start a background watcher that monitors a folder and auto-updates the graph when files change. 1148→ 1149→```bash 1150→python3 -m graphify.watch INPUT_PATH --debounce 3 1151→``` 1152→ 1153→Replace INPUT_PATH with the folder to watch. Behavior depends on what changed: 1154→ 1155→- **Code files only (.py, .ts, .go, etc.):** re-runs AST extraction + rebuild + cluster immediately, no LLM needed. `graph.json` and `GRAPH_REPORT.md` are updated automatically. 1156→- **Docs, papers, or images:** writes a `graphify-out/needs_update` flag and prints a notification to run `/graphify --update` (LLM semantic re-extraction required). 1157→ 1158→Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file. 1159→ 1160→Press Ctrl+C to stop. 1161→ 1162→For agentic workflows: run `--watch` in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual `/graphify --update` after those waves. 1163→ 1164→--- 1165→ 1166→## For git commit hook 1167→ 1168→Install a post-commit hook that auto-rebuilds the graph after every commit. No background process needed - triggers once per commit, works with any editor. 1169→ 1170→```bash 1171→graphify hook install # install 1172→graphify hook uninstall # remove 1173→graphify hook status # check 1174→``` 1175→ 1176→After every `git commit`, the hook detects which code files changed (via `git diff HEAD~1`), re-runs AST extraction on those files, and rebuilds `graph.json` and `GRAPH_REPORT.md`. Doc/image changes are ignored by the hook - run `/graphify --update` manually for those. 1177→ 1178→If a post-commit hook already exists, graphify appends to it rather than replacing it. 1179→ 1180→--- 1181→ 1182→## For native CLAUDE.md integration 1183→ 1184→Run once per project to make graphify always-on in Claude Code sessions: 1185→ 1186→```bash 1187→graphify claude install 1188→``` 1189→ 1190→This writes a `## graphify` section to the local `CLAUDE.md` that instructs Claude to check the graph before answering codebase questions and rebuild it after code changes. No manual `/graphify` needed in future sessions. 1191→ 1192→```bash 1193→graphify claude uninstall # remove the section 1194→``` 1195→ 1196→--- 1197→ 1198→## Honesty Rules 1199→ 1200→- Never invent an edge. If unsure, use AMBIGUOUS. 1201→- Never skip the corpus check warning. 1202→- Always show token cost in the report. 1203→- Never hide cohesion scores behind symbols - show the raw number. 1204→- Never run HTML viz on a graph with more than 5,000 nodes without warning the user. 1205→ 1206→---HTTP_STATUS:200---