Graphify

/graphify

{SKILL_DIR} = this skill's own folder (locally: the directory this SKILL.md lives in; a served brain substitutes the real absolute path before you read this). If a path does not resolve, name the missing file and stop — never guess another location.

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.

⚡ Before running on a corpus larger than ~50 files, READ OPTIMIZATIONS.md. The wrapper scripts in scripts/ (dedupe, regex preflight, word-balanced chunking, label canonicalization, cache integration) typically cut LLM token cost by 80–92% and produce a higher-quality graph. The single most important step is calling graphify_canonicalize.py --cache after merging — without it, the next --update run repays the entire cost. Skip these wrappers and a 1,500-file vault will burn ~10M LLM tokens for the same graph that costs ~1M with them.

Usage

/graphify                                             # full pipeline on current directory → Obsidian vault
/graphify <path>                                      # full pipeline on specific path
/graphify <path> --mode deep                          # thorough extraction, richer INFERRED edges
/graphify <path> --update                             # incremental - re-extract only new/changed files
/graphify <path> --directed                            # build directed graph (preserves edge direction: source→target)
/graphify <path> --whisper-model medium                # use a larger Whisper model for better transcription accuracy (base|small|medium|large)
/graphify <path> --cluster-only                       # rerun clustering on existing graph
/graphify <path> --no-viz                             # skip visualization, just report + JSON
/graphify <path> --html                               # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml                            # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j                              # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/graphify add <url>                                   # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                   # tag who wrote it
/graphify add <url> --contributor "Name"              # tag who added it to the corpus
/graphify query "<question>"                          # BFS traversal - broad context
/graphify query "<question>" --dfs                    # DFS - trace a specific path
/graphify query "<question>" --budget 1500            # cap answer at N tokens
/graphify path "AuthModule" "Database"                # shortest path between two concepts
/graphify explain "SwinTransformer"                   # plain-language explanation of a node

What graphify is for

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.

Three things it does that Claude alone cannot:

1. Persistent graph - relationships are stored in graphify-out/graph.json and survive across sessions. Ask questions weeks later without re-reading everything.
2. Honest audit trail - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
3. Cross-document surprise - community detection finds connections between concepts in different files that you would never think to ask about directly.

Use it for:

• A codebase you're new to (understand architecture before touching anything)
• A reading list (papers + tweets + notes → one navigable graph)
• A research corpus (citation graph + concept graph in one)
• Your personal /raw folder (drop everything in, let it grow, query it)

CRITICAL: graph.json edge key

graphify.export.to_json() uses networkx's node_link_data format. Edges are stored under the key "links", NOT "edges". If you ever write a custom script that loads graph.json and reads edges directly, use:

edges = graph.get('links', graph.get('edges', []))  # 'links' first — 'edges' is never set by to_json

Failing to do this gives you 0 edges silently and will corrupt any merge.

The skill's own pipeline uses internal temp files (.graphify_*.json) that DO use "edges" — that is fine because those files are written/read by the same code. This warning only applies to loading graph.json directly.

What You Must Do When Invoked

If no path was given, use . (current directory). Do not ask the user for a path.

Follow these steps in order. Do not skip steps.

Step 1 - Ensure graphify is installed

# Detect the correct Python interpreter (handles pipx, venv, system installs)
GRAPHIFY_BIN=$(which graphify 2>/dev/null)
if [ -n "$GRAPHIFY_BIN" ]; then
    PYTHON=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
    case "$PYTHON" in
        *[!a-zA-Z0-9/_.-]*) PYTHON="python3" ;;
    esac
else
    PYTHON="python3"
fi
mkdir -p graphify-out

# Install path with real error surfacing. On failure, STOP and tell the user —
# do not continue with a broken interpreter. Historical bug: pip errors were
# swallowed by tail -3 + silent || chaining and the skill kept going.
if ! "$PYTHON" -c "import graphify" 2>/dev/null; then
    echo "graphify not importable — attempting install..."
    INSTALL_LOG="graphify-out/.graphify_install.log"
    if ! "$PYTHON" -m pip install graphifyy -q 2>"$INSTALL_LOG"; then
        # PEP 668 environments (macOS system python, Debian 12+) need the flag
        if ! "$PYTHON" -m pip install graphifyy -q --break-system-packages 2>>"$INSTALL_LOG"; then
            echo ""
            echo "ERROR: could not install graphify. Last install output:"
            echo "---"
            tail -20 "$INSTALL_LOG"
            echo "---"
            echo ""
            echo "Common fixes:"
            echo "  • Network / corporate proxy: check your pip config"
            echo "  • Permissions: try 'pipx install graphifyy' instead"
            echo "  • Python version: graphify needs Python 3.10+"
            echo ""
            echo "Stopping. Do not re-run /graphify until the import works:"
            echo "  $PYTHON -c 'import graphify'"
            exit 2
        fi
    fi
    # Verify install actually took
    if ! "$PYTHON" -c "import graphify" 2>/dev/null; then
        echo "ERROR: graphify installed but still not importable by $PYTHON."
        echo "This usually means a venv / pyenv mismatch. Run:"
        echo "  which python3 && $PYTHON -c 'import sys; print(sys.path)'"
        exit 2
    fi
    echo "graphify installed successfully."
fi

# Write interpreter path for all subsequent steps
"$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w').write(sys.executable)"

If the import succeeds on first try, print nothing and move straight to Step 2. If an install ran, the success line is the only output. If install failed, the skill has already exited — do not continue.

In every subsequent bash block, replace python3 with $(cat graphify-out/.graphify_python) to use the correct interpreter.

Step 2 - Detect files

$(cat graphify-out/.graphify_python) -c "
import json
from graphify.detect import detect
from pathlib import Path
result = detect(Path('INPUT_PATH'))
print(json.dumps(result))
" > graphify-out/.graphify_detect.json

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:

Corpus: X files · ~Y words
  code:     N files (.py .ts .go ...)
  docs:     N files (.md .txt ...)
  papers:   N files (.pdf ...)
  images:   N files

Then act on it:

• If total_files is 0: stop with "No supported files found in [path]."
• If skipped_sensitive is non-empty: mention file count skipped, not the file names.
• 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.
• Otherwise: proceed directly to Step 2.5 if the corpus has video files, else straight to Step 3 - no need to ask anything.

Step 2.5 - Transcribe video/audio files (only if detect reported video files)

Skip this step entirely if .graphify_detect.json's files.video list is empty. Video and audio cannot be read directly - transcribe first, then treat the transcripts as doc files in Step 3. See TRANSCRIBE.md for the full procedure (writing a domain-hint prompt, --whisper-model handling, running graphify.transcribe.transcribe_all).

Step 3 - Extract entities and relationships

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.

This step has two parts: structural extraction (deterministic, free) and semantic extraction (Claude, costs tokens).

Run Part A (AST), Part A.5 (zero-LLM typed edges, if available), and Part B (semantic) in parallel. Dispatch all semantic subagents AND start the structural extractions in the same message. They operate on different surfaces — AST on code files, Part A.5 on markdown frontmatter + wikilinks, Part B on docs/papers/images via subagents — so they can run simultaneously. Merge results in Part C as before.

Note: Parallelizing structural + semantic saves 5-15s on large corpora. The structural passes are deterministic and fast; start them while subagents process the rest.

Part A - Structural extraction for code files

For any code files detected, run AST extraction in parallel with Part B subagents:

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.extract import collect_files, extract
from pathlib import Path
import json

code_files = []
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text())
for f in detect.get('files', {}).get('code', []):
    code_files.extend(collect_files(Path(f)) if Path(f).is_dir() else [Path(f)])

if code_files:
    result = extract(code_files)
    Path('graphify-out/.graphify_ast.json').write_text(json.dumps(result, indent=2))
    print(f'AST: {len(result[\"nodes\"])} nodes, {len(result[\"edges\"])} edges')
else:
    Path('graphify-out/.graphify_ast.json').write_text(json.dumps({'nodes':[],'edges':[],'input_tokens':0,'output_tokens':0}))
    print('No code files - skipping AST extraction')
"

Part A.5 - Zero-LLM typed-relationship extraction for markdown

If a typed-relationship extractor is available, run it in parallel with Part A and Part B. It walks markdown, reads frontmatter + body, and emits typed edges (works_at, floor_at, journaled_about, attended, mentions, governs, created_on, investor_for) to JSONL at zero LLM cost. Extracts ~80% of explicit edges (frontmatter + wikilinks); Part B's semantic extraction is configured to skip entities + edges already covered by this pass, so the LLM only fires on the genuinely-ambiguous ~20% (proper-noun disambiguation, novel entity types, negation parsing).

# Locate the shipped script; PATH fallback for users who installed it standalone.
WIRE_SCRIPT=""
for candidate in \
    "{SKILL_DIR}/scripts/wire_typed_relationships.py" \
    "$HOME/.claude/plugins/graphify/scripts/wire_typed_relationships.py" \
    "./scripts/wire_typed_relationships.py"; do
    if [ -f "$candidate" ]; then
        WIRE_SCRIPT="$candidate"
        break
    fi
done
if [ -z "$WIRE_SCRIPT" ] && command -v wire_typed_relationships.py >/dev/null 2>&1; then
    WIRE_SCRIPT="$(command -v wire_typed_relationships.py)"
fi

if [ -n "$WIRE_SCRIPT" ]; then
    "$(cat graphify-out/.graphify_python)" "$WIRE_SCRIPT" \
        --root "INPUT_PATH" \
        --output graphify-out/.graphify_typed_edges.jsonl \
        2>&1 | tail -5
fi

Pattern source: garrytan/gbrain — zero-LLM graph wiring. The script itself ships at scripts/wire_typed_relationships.py inside this skill; it's standalone and zero-dep (stdlib only) so it runs in any Python 3.10+ environment.

If graphify-out/.graphify_typed_edges.jsonl exists after this step, Part B's dispatch must pass the path to each subagent so they honor the skip list. Cost savings only materialize if the skip list is honored — otherwise this pass just duplicates work.

This JSONL is a skip list for Part B, nothing merges it into the graph automatically. After Step 4 builds graph.json, run Step 4b to generate a human-reviewable report of which of these edges can be safely merged.

Part B - Semantic extraction (parallel subagents)

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.

If Part A.5 ran: pass the path to graphify-out/.graphify_typed_edges.jsonl to each subagent as a "skip list" so they don't re-extract what regex already covered. This is where the cost savings happen — Part A.5 by itself just creates a redundant edge set if Part B ignores the file.

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.

Before dispatching subagents, print a timing estimate:

• Load total_words and file counts from graphify-out/.graphify_detect.json
• Estimate agents needed: ceil(uncached_non_code_files / 22) (chunk size is 20-25)
• Estimate time: ~45s per agent batch (they run in parallel, so total ≈ 45s × ceil(agents/parallel_limit))
• Print: "Semantic extraction: ~N files → X agents, estimated ~Ys"

Step B0 - Check extraction cache first

Before dispatching any subagents, check which files already have cached extraction results:

$(cat graphify-out/.graphify_python) -c "
import json
from graphify.cache import check_semantic_cache
from pathlib import Path

detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text())
all_files = [f for files in detect['files'].values() for f in files]

cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)

if cached_nodes or cached_edges or cached_hyperedges:
    Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}))
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached))
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"

Only dispatch subagents for files listed in graphify-out/.graphify_uncached.txt. If all files are cached, skip to Part C directly.

Step B1 - Split into chunks

Load files from graphify-out/.graphify_uncached.txt. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). When splitting, group files from the same directory together so related artifacts land in the same chunk and cross-file relationships are more likely to be extracted.

Step B1b - Recovery check (compaction guard)

Before dispatching, check if chunk files from a previous interrupted run already exist:

ls graphify-out/.graphify_chunk_*.json 2>/dev/null | wc -l

If files exist: print "Found N chunk files from previous run — skipping dispatch for those chunks." Skip to Step B3 to merge what's already on disk. Only re-dispatch chunks whose file is missing.

Step B2 - Dispatch ALL subagents in a single message

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.

Concrete example for 3 chunks:

[Agent tool call 1: files 1-15]
[Agent tool call 2: files 16-30]  
[Agent tool call 3: files 31-45]

All three in one message. Not three separate messages.

Rate-limit awareness (cold-start users on low API tiers). If many subagents return errors mentioning rate_limit, 429, overloaded_error, or retry later, do NOT pretend the extraction succeeded. Surface the issue plainly:

"Several subagents failed with rate-limit errors. Your Anthropic API tier is capping concurrent requests below what this vault needs. Options:

1. Wait 1 minute and re-run /graphify --update — graphify caches completed chunks on disk, so only the failed ones re-dispatch.
2. Split the corpus: /graphify Notes/ then /graphify Journals/ in separate runs.
3. Raise your API tier at https://console.anthropic.com/settings/limits — Tier 2+ is enough for ~1000-file vaults."

Do not try to workaround by reading files yourself. The cache handles partial runs gracefully. The user makes the tier decision.

Each subagent receives this exact prompt (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, and DEEP_MODE):

You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment.
Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble.

Files (chunk CHUNK_NUM of TOTAL_CHUNKS):
FILE_LIST

Rules:
- EXTRACTED: relationship explicit in source (import, call, citation, "see §3.2")
- INFERRED: reasonable inference (shared data structure, implied dependency)
- AMBIGUOUS: uncertain - flag for review, do not omit

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. Also extract rationale — sections that explain WHY a decision was made, trade-offs chosen, or design intent. These become nodes with `rationale_for` edges pointing to the concept they explain.
Image files: use vision to understand what the image IS - do not just OCR.
  UI screenshot: layout patterns, design decisions, key elements, purpose.
  Chart: metric, trend/insight, data source.
  Tweet/post: claim as node, author, concepts mentioned.
  Diagram: components and connections.
  Research figure: what it demonstrates, method, result.
  Handwritten/whiteboard: ideas and arrows, mark uncertain readings AMBIGUOUS.

DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.

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:
- Two functions that both validate user input but never call each other
- A class in code and a concept in a paper that describe the same algorithm
- Two error types that handle the same failure mode differently
Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.

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:
- All classes that implement a common protocol or interface
- All functions in an authentication flow (even if they don't all call each other)
- All concepts from a paper section that form one coherent idea
Use sparingly — only when the group relationship adds information beyond the pairwise edges. Maximum 3 hyperedges per chunk.

If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
  contributor onto every node from that file.

confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a default:
- EXTRACTED edges: confidence_score = 1.0 always
- INFERRED edges: reason about each edge individually.
  Direct structural evidence (shared data structure, clear dependency): 0.8-0.9.
  Reasonable inference with some uncertainty: 0.6-0.7.
  Weak or speculative: 0.4-0.5. Most edges should be 0.6-0.9, not 0.5.
- 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}

**After outputting the JSON, write it to disk as your final action** — this protects against data loss if the parent session undergoes context compaction before collecting results. Use the Write tool (or Bash) to save to `graphify-out/.graphify_chunk_CHUNK_NUM.json` (replace CHUNK_NUM with the actual chunk number substituted into this prompt). If the directory does not exist, create it first.

Step B3 - Collect, cache, and merge

Wait for all subagents. Then collect results from disk (chunk files written by each subagent), not from the agent return values in memory. This is compaction-safe: if Claude compacts context between subagent completion and this step, the data is already on disk.

$(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path

chunk_files = sorted(Path('graphify-out').glob('.graphify_chunk_*.json'))
all_nodes, all_edges, all_hyperedges, in_tok, out_tok = [], [], [], 0, 0
failed = []

for f in chunk_files:
    try:
        data = json.loads(f.read_text())
        if 'nodes' not in data and 'edges' not in data:
            raise ValueError('missing nodes/edges keys')
        all_nodes.extend(data.get('nodes', []))
        all_edges.extend(data.get('edges', []))
        all_hyperedges.extend(data.get('hyperedges', []))
        in_tok += data.get('input_tokens', 0)
        out_tok += data.get('output_tokens', 0)
    except Exception as e:
        failed.append(f.name)
        print(f'Warning: skipping {f.name}: {e}')

if failed:
    print(f'Failed chunks ({len(failed)}): {failed}')
if len(failed) > len(chunk_files) // 2:
    raise SystemExit('More than half the chunks failed. Stopping.')

result = {'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges,
          'input_tokens': in_tok, 'output_tokens': out_tok}
Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps(result))
print(f'Collected {len(all_nodes)} nodes, {len(all_edges)} edges from {len(chunk_files)} chunks ({len(failed)} failed)')
"

If more than half the chunks have no file on disk (subagent never wrote), stop and tell the user.

Save new results to cache:

$(cat graphify-out/.graphify_python) -c "
import json
from graphify.cache import save_semantic_cache
from pathlib import Path

new = json.loads(Path('graphify-out/.graphify_semantic_new.json').read_text()) if Path('graphify-out/.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
# root= is REQUIRED - default Path('.') breaks if CWD is not vault root
saved = save_semantic_cache(new.get('nodes', []), new.get('edges', []), new.get('hyperedges', []), root=Path.cwd())
print(f'Cached {saved} files')
"

Merge cached + new results into graphify-out/.graphify_semantic.json:

$(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path

cached = json.loads(Path('graphify-out/.graphify_cached.json').read_text()) if Path('graphify-out/.graphify_cached.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
new = json.loads(Path('graphify-out/.graphify_semantic_new.json').read_text()) if Path('graphify-out/.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}

all_nodes = cached['nodes'] + new.get('nodes', [])
all_edges = cached['edges'] + new.get('edges', [])
all_hyperedges = cached.get('hyperedges', []) + new.get('hyperedges', [])
seen = set()
deduped = []
for n in all_nodes:
    if n['id'] not in seen:
        seen.add(n['id'])
        deduped.append(n)

merged = {
    'nodes': deduped,
    'edges': all_edges,
    'hyperedges': all_hyperedges,
    'input_tokens': new.get('input_tokens', 0),
    'output_tokens': new.get('output_tokens', 0),
}
Path('graphify-out/.graphify_semantic.json').write_text(json.dumps(merged, indent=2))
print(f'Extraction complete - {len(deduped)} nodes, {len(all_edges)} edges ({len(cached[\"nodes\"])} from cache, {len(new.get(\"nodes\",[]))} new)')
"

Clean up temp files: rm -f graphify-out/.graphify_cached.json graphify-out/.graphify_uncached.txt graphify-out/.graphify_semantic_new.json graphify-out/.graphify_chunk_*.json

Part C - Merge AST + semantic into final extraction

$(cat graphify-out/.graphify_python) -c "
import sys, json
from pathlib import Path

ast = json.loads(Path('graphify-out/.graphify_ast.json').read_text())
sem = json.loads(Path('graphify-out/.graphify_semantic.json').read_text())

# Merge: AST nodes first, semantic nodes deduplicated by id
seen = {n['id'] for n in ast['nodes']}
merged_nodes = list(ast['nodes'])
for n in sem['nodes']:
    if n['id'] not in seen:
        merged_nodes.append(n)
        seen.add(n['id'])

merged_edges = ast['edges'] + sem['edges']
merged_hyperedges = sem.get('hyperedges', [])
merged = {
    'nodes': merged_nodes,
    'edges': merged_edges,
    'hyperedges': merged_hyperedges,
    'input_tokens': sem.get('input_tokens', 0),
    'output_tokens': sem.get('output_tokens', 0),
}
Path('graphify-out/.graphify_extract.json').write_text(json.dumps(merged, indent=2))
total = len(merged_nodes)
edges = len(merged_edges)
print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(sem[\"nodes\"])} semantic)')
"

Step 4 - Build graph, cluster, analyze, generate outputs

Before starting: note whether --directed was given in the original invocation. Every build_from_json(...) call from here through the exports in Step 7 uses directed=IS_DIRECTED — replace IS_DIRECTED with the literal True if --directed was given, otherwise False (the default, an undirected graph). Do NOT leave the literal placeholder IS_DIRECTED in the code. Track this the same way you tracked DEEP_MODE in Step 3 - do not lose it. Skipping this silently rebuilds an undirected graph even when the user asked for directed, and on --update it silently collapses reciprocal A<->B edges into one.

mkdir -p graphify-out
$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import cluster, score_all
from graphify.analyze import god_nodes, surprising_connections, suggest_questions
from graphify.report import generate
from graphify.export import to_json
from pathlib import Path

extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text())
detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text())

G = build_from_json(extraction, directed=IS_DIRECTED)
communities = cluster(G)
cohesion = score_all(G, communities)
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
gods = god_nodes(G)
surprises = surprising_connections(G, communities)
sys.path.insert(0, '{SKILL_DIR}/scripts')
from graphify_seed_labels import seed_labels
# Provisional names from each community's highest-degree member ('Money', 'Fear'),
# never 'Community 412'. Step 5 replaces these with semantic labels.
labels = seed_labels(G, communities)
# Placeholder questions - regenerated with real labels in Step 5
questions = suggest_questions(G, communities, labels)

report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report)
to_json(G, communities, 'graphify-out/graph.json')

analysis = {
    'communities': {str(k): v for k, v in communities.items()},
    'cohesion': {str(k): v for k, v in cohesion.items()},
    'gods': gods,
    'surprises': surprises,
    'questions': questions,
}
Path('graphify-out/.graphify_analysis.json').write_text(json.dumps(analysis, indent=2))
if G.number_of_nodes() == 0:
    print('ERROR: Graph is empty - extraction produced no nodes.')
    print('Possible causes: all files were skipped, binary-only corpus, or extraction failed.')
    raise SystemExit(1)
print(f'Graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges, {len(communities)} communities')
"

Then make the report safe to sit inside a vault:

python3 "{SKILL_DIR}/scripts/graphify_report_sanitize.py" graphify-out/GRAPH_REPORT.md

The upstream report links a [[_COMMUNITY_*]] hub note per community. Those notes exist only in the opt-in Obsidian export of Step 6, so on a default run every one of them is an unresolved link — and Obsidian draws each unresolved link as a node. On a real vault that is thousands of grey placeholder dots radiating from one file, which is what the user's graph view then shows instead of their notes. The sanitizer emits a wikilink only when the target note actually exists, lists communities at or above a node floor (default 5, since most detected communities are 1–2 node extraction fragments), and excludes graphify-out/ from the vault index. Stdlib only — plain python3, not the graphify venv.

Never skip this on a corpus that is also someone's Obsidian vault.

If the user already has a report full of Community 0 / Community 412 names from an earlier run, rename it in place — no re-extraction needed:

python3 "{SKILL_DIR}/scripts/graphify_report_sanitize.py" graphify-out/GRAPH_REPORT.md \
  --relabel-from graphify-out/graph.json

If this step prints ERROR: Graph is empty, stop and tell the user what happened - do not proceed to labeling or visualization.

Replace INPUT_PATH with the actual path.

Step 4b - Review Part A.5's typed edges (if Part A.5 ran)

Part A.5's typed edges are NOT auto-merged into graph.json — see the "Part A.5" section above for why: node IDs from Part B are LLM-improvised per entity (not a deterministic function of the source file), so a wikilink target can match zero, one, or several nodes, and the wikilink-derived edge types (mentions, journaled_about, attended, investor_for) already appear in graph.json via Part B with an entity-points-at-document direction that Part A.5's raw file-points-at-wikilink reading contradicts unless flipped. Blind-merging risks corrupting a graph the user relies on.

If graphify-out/.graphify_typed_edges.jsonl exists, run the review script instead:

$(cat graphify-out/.graphify_python) "{SKILL_DIR}/scripts/review_typed_edges.py" \
    --graph graphify-out/graph.json \
    --edges graphify-out/.graphify_typed_edges.jsonl \
    --output graphify-out/TYPED_EDGES_REVIEW.md

This resolves what it safely can (unique src/dst match, not already present, direction corrected for the wikilink types) into a "ready to merge" table, and buckets the rest as ambiguous / dst-not-found / src-not-found — mirroring the review-then-apply shape of WIKILINK_GAPS.md. Tell the user the report is ready and summarize the bucket counts; do not write anything into graph.json without the user reviewing TYPED_EDGES_REVIEW.md and saying which rows to apply.

Step 5 - Label communities

Read graphify-out/.graphify_analysis.json.

Label the communities that are real topics, not every key. Community detection on a personal corpus produces a long tail of fragments: measured on a 14,046-node vault, 51% of communities held a SINGLE node and 96% held 4 or fewer. Naming those is busywork that produces junk names, and it is why unlabeled runs end up showing "Community 412".

• Sort communities by node count, descending.
• Label every community with >= 5 nodes, up to a ceiling of 50 communities. On the measured vault that is 101 communities holding 70% of all nodes — the whole map, at ~4% of the naming work.
• Leave the rest out of LABELS_DICT. They keep the provisional anchor name from Step 4 and are omitted from navigation by the sanitizer.

For each community you do label, look at its node labels and write a 2-5 word plain-language name (e.g. "Attention Mechanism", "Training Pipeline", "Data Loading"). The Step 4 anchor name is a starting hint, not an answer — a semantic label beats it every time.

Then regenerate the report and save the labels for the visualizer:

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import score_all
from graphify.analyze import god_nodes, surprising_connections, suggest_questions
from graphify.report import generate
from pathlib import Path

extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text())
detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text())
analysis   = json.loads(Path('graphify-out/.graphify_analysis.json').read_text())

G = build_from_json(extraction, directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}

# LABELS - replace these with the names you chose above
labels = LABELS_DICT

# Regenerate questions with real community labels (labels affect question phrasing)
questions = suggest_questions(G, communities, labels)

report = generate(G, communities, cohesion, labels, analysis['gods'], analysis['surprises'], detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report)
Path('graphify-out/.graphify_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()}))
print('Report updated with community labels')
"

python3 "{SKILL_DIR}/scripts/graphify_report_sanitize.py" graphify-out/GRAPH_REPORT.md

generate() rewrites the whole report, so the sanitizer runs again here — otherwise this step puts every ghost link straight back. Verify with --check; it exits 1 if any unresolved community link survived:

python3 "{SKILL_DIR}/scripts/graphify_report_sanitize.py" --check graphify-out/GRAPH_REPORT.md

Replace LABELS_DICT with the actual dict you constructed (e.g. {0: "Attention Mechanism", 1: "Training Pipeline"}). Replace INPUT_PATH with the actual path.

Step 6 - Generate Obsidian vault (opt-in) + HTML

Generate HTML always (unless --no-viz). Obsidian vault only if --obsidian was explicitly given — skip it otherwise, it generates one file per node.

If --obsidian was given:

• If --obsidian-dir <path> was also given, use that path as the vault directory. Otherwise default to graphify-out/obsidian.

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_obsidian, to_canvas
from pathlib import Path

extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text())
analysis   = json.loads(Path('graphify-out/.graphify_analysis.json').read_text())
labels_raw = json.loads(Path('graphify-out/.graphify_labels.json').read_text()) if Path('graphify-out/.graphify_labels.json').exists() else {}

G = build_from_json(extraction, directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
labels = {int(k): v for k, v in labels_raw.items()}

obsidian_dir = 'OBSIDIAN_DIR'  # replace with --obsidian-dir value, or 'graphify-out/obsidian' if not given

n = to_obsidian(G, communities, obsidian_dir, community_labels=labels or None, cohesion=cohesion)
print(f'Obsidian vault: {n} notes in {obsidian_dir}/')

to_canvas(G, communities, f'{obsidian_dir}/graph.canvas', community_labels=labels or None)
print(f'Canvas: {obsidian_dir}/graph.canvas - open in Obsidian for structured community layout')
print()
print(f'Open {obsidian_dir}/ as a vault in Obsidian.')
print('  Graph view   - nodes colored by community (set automatically)')
print('  graph.canvas - structured layout with communities as groups')
print('  _COMMUNITY_* - overview notes with cohesion scores and dataview queries')
"

The hub notes now exist, so the report's navigation links can be live. Re-run the sanitizer pointed at the export to turn them back into working wikilinks:

python3 "{SKILL_DIR}/scripts/graphify_report_sanitize.py" graphify-out/GRAPH_REPORT.md \
  --obsidian-dir OBSIDIAN_DIR

Generate the HTML graph (always, unless --no-viz):

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_html
from pathlib import Path

extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text())
analysis   = json.loads(Path('graphify-out/.graphify_analysis.json').read_text())
labels_raw = json.loads(Path('graphify-out/.graphify_labels.json').read_text()) if Path('graphify-out/.graphify_labels.json').exists() else {}

G = build_from_json(extraction, directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
labels = {int(k): v for k, v in labels_raw.items()}

if G.number_of_nodes() > 5000:
    print(f'Graph has {G.number_of_nodes()} nodes - too large for HTML viz. Use Obsidian vault instead.')
else:
    to_html(G, communities, 'graphify-out/graph.html', community_labels=labels or None)
    print('graph.html written - open in any browser, no server needed')
"

Step 7 - Neo4j export (only if --neo4j or --neo4j-push flag)

If --neo4j - generate a Cypher file for manual import:

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_cypher
from pathlib import Path

G = build_from_json(json.loads(Path('graphify-out/.graphify_extract.json').read_text()), directed=IS_DIRECTED)
to_cypher(G, 'graphify-out/cypher.txt')
print('cypher.txt written - import with: cypher-shell < graphify-out/cypher.txt')
"

If --neo4j-push <uri> - push directly to a running Neo4j instance. Ask the user for credentials if not provided:

$(cat graphify-out/.graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import cluster
from graphify.export import push_to_neo4j
from pathlib import Path

extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text())
analysis   = json.loads(Path('graphify-out/.graphify

…(truncated)