Imported from coderXdevelop/chatapp (.claude/skills/graphify/SKILL.md). Install upstream with npx skills add coderXdevelop/chatapp --skill graphify. Copyright stays with the author.

/graphify

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.

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> --pdf-ocr auto                       # preflight PDFs; OCR scanned/low-text PDFs with mistral-ocr when needed
/graphify <path> --update                             # incremental - re-extract only new/changed files
/graphify <path> --directed                           # build directed graph (preserves source→target)
/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/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
graphify wiki describe --graph .graphify/graph.json --mode assistant --targets all  # opt-in description sidecars
graphify export wiki --graph .graphify/graph.json --descriptions .graphify/wiki/descriptions.json
graphify export obsidian --graph .graphify/graph.json --descriptions .graphify/wiki/descriptions.json
/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/graphify <path> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/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 migrate-state --dry-run                    # plan graphify-out -> .graphify migration
/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 summary --graph .graphify/graph.json        # compact first-hop orientation before deep traversal
/graphify minimal-context --task "review PR" --graph .graphify/graph.json  # first review call
/graphify review-delta --files src/auth.ts --graph .graphify/graph.json  # review impact for changed files
/graphify review-analysis --files src/auth.ts --graph .graphify/graph.json  # blast radius + review views
/graphify recommend-commits --files src/auth.ts,src/session.ts --graph .graphify/graph.json  # advisory commit grouping
/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:

Persistent graph - relationships are stored in .graphify/graph.json and survive across sessions. Ask questions weeks later without re-reading everything.
Honest audit trail - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
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)

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

if (-not (Get-Command graphify -ErrorAction SilentlyContinue)) { npm install -g @sentropic/graphify 2>&1 | Select-Object -Last 3 }
New-Item -ItemType Directory -Force -Path .graphify | Out-Null

If the import succeeds, print nothing and move straight to Step 2.

Step 2 - Detect files

node -e "
const { detect } = require('@sentropic/graphify');
const result = detect('INPUT_PATH');
console.log(JSON.stringify(result));
" | Out-File -FilePath .graphify/.graphify_detect.json -Encoding utf8

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
  video:    N files (.mp4 .mp3 ...)

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 to Step 2.5. It is a safe no-op if no video or PDF files need preprocessing.

Step 2.5 - Prepare semantic detection, including audio/video transcripts and PDF preflight/OCR when needed

Always run this step. It transcribes audio/video when present, runs local PDF preflight for papers, and converts text-layer PDFs locally. For scanned/low-text PDFs, keep two OCR paths explicit: the assistant vision model may interpret the original PDF or extracted image chunks during semantic extraction, and mistral-ocr may be used when OCR is explicitly requested or when a configured MISTRAL_API_KEY is available and preflight detects a scanned/low-text PDF. Do not call Mistral blindly. In auto mode, if no key/provider is available, leave the source PDF in semantic inputs so the assistant path can still handle it. Generated transcripts, PDF Markdown sidecars, and relevant PDF-extracted images are treated as semantic inputs in Step 3.

node -e "
(async () => {
const fs = require('fs');
const { prepareSemanticDetection } = require('@sentropic/graphify');

const detect = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));
const analysis = fs.existsSync('.graphify/.graphify_analysis.json')
  ? JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'))
  : null;

const { detection: semanticDetect, transcriptPaths, pdfArtifacts } = await prepareSemanticDetection(detect, {
  transcriptOutputDir: '.graphify/transcripts',
  pdfOutputDir: '.graphify/converted/pdf',
  godNodes: (analysis && analysis.gods) || [],
});

fs.writeFileSync('.graphify/.graphify_detect_semantic.json', JSON.stringify(semanticDetect, null, 2));
fs.writeFileSync('.graphify/.graphify_transcripts.json', JSON.stringify(transcriptPaths, null, 2));
fs.writeFileSync('.graphify/.graphify_pdf_ocr.json', JSON.stringify(pdfArtifacts, null, 2));
console.log('Prepared semantic inputs: ' + transcriptPaths.length + ' transcript(s), ' + pdfArtifacts.filter((item) => item.markdownPath).length + ' PDF sidecar(s)');
)().catch((error) => {
  console.error(error instanceof Error ? error.message : String(error));
  process.exit(1);
});
"

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) 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.

Note: Parallelizing AST + semantic saves 5-15s on large corpora. AST is deterministic and fast; start it while subagents are processing docs/papers.

Part A - Structural extraction for code files

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

node -e "(async () => {
const fs = require('fs');
const { collectFiles, extract } = require('@sentropic/graphify');

const detect = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));
let codeFiles = [];
for (const f of (detect.files || {}).code || []) {
    codeFiles = codeFiles.concat(collectFiles(f));
}

if (codeFiles.length > 0) {
    const result = await extract(codeFiles);
    fs.writeFileSync('.graphify/.graphify_ast.json', JSON.stringify(result, null, 2));
    console.log(\`AST: ${result.nodes.length} nodes, ${result.edges.length} edges\`);
} else {
    fs.writeFileSync('.graphify/.graphify_ast.json', JSON.stringify({nodes:[],edges:[],input_tokens:0,output_tokens:0}));
    console.log('No code files - skipping AST extraction');
}
})()"

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. Fast path: If semantic detection found zero docs, papers, images, and transcripts (code-only corpus), skip Part B entirely and go straight to Part C. AST handles code - there is nothing for semantic subagents to do.

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/.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:

node -e "
const fs = require('fs');
const { checkSemanticCache } = require('@sentropic/graphify');

const detect = JSON.parse(fs.readFileSync('.graphify/.graphify_detect_semantic.json', 'utf-8'));
const allFiles = [
  ...((detect.files || {}).document || []),
  ...((detect.files || {}).paper || []),
  ...((detect.files || {}).image || []),
];

const [cachedNodes, cachedEdges, cachedHyperedges, uncached] = checkSemanticCache(allFiles);

if (cachedNodes.length || cachedEdges.length || cachedHyperedges.length) {
    fs.writeFileSync('.graphify/.graphify_cached.json', JSON.stringify({nodes: cachedNodes, edges: cachedEdges, hyperedges: cachedHyperedges}));
}
fs.writeFileSync('.graphify/.graphify_uncached.txt', uncached.join('\n'));
console.log(\`Cache: ${allFiles.length - uncached.length} files hit, ${uncached.length} files need extraction\`);
"

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

Step B1 - Split into chunks

Load files from .graphify/.graphify_uncached.txt. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). PDF sidecar Markdown can reference extracted image artifacts under .graphify/converted/pdf/*_images/; when those images contain diagrams, tables, captions, or embedded text that carry meaning, include them as image chunks or describe the delegated OCR/vision output with provenance back to the source PDF. Keep files from the same directory together when possible so related artifacts land in the same chunk.

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.

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. For rationale (WHY decisions were made, trade-offs, design intent): store it as a `rationale` attribute on the relevant concept node. Do not create a separate rationale node or fragment node unless it is itself a named concept.
Code files: when adding `calls` edges, source MUST be the caller and target MUST be the callee. Never reverse the direction.
Image files: use vision to understand what the image IS - do not just OCR. For images extracted from PDFs, decode figures, tables, diagrams, captions, and embedded text when they carry meaning; use the assistant vision model by default, or a delegated OCR/vision model when configured, and keep provenance to the source PDF and sidecar.
  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|concept|rationale","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null,"rationale":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

Wait for all subagents. For each result:

If a subagent returned valid JSON with nodes and edges, include it and save each file's nodes/edges to the cache
If a subagent failed or returned invalid JSON, print a warning and skip that chunk - do not abort

If more than half the chunks failed, stop and tell the user.

Save new results to cache. The saveSemanticCache call is wrapped in validateSemanticFragment + sanitizeSemanticFragment so a malformed agent response cannot poison the per-file semantic cache:

node -e "
const fs = require('fs');
const { saveSemanticCache, validateSemanticFragment, sanitizeSemanticFragment } = require('@sentropic/graphify');

const raw = fs.existsSync('.graphify/.graphify_semantic_new.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_semantic_new.json', 'utf-8')) : {nodes:[],edges:[],hyperedges:[]};
const errors = validateSemanticFragment(raw);
if (errors.length > 0) {
  console.error('Refusing to cache invalid semantic fragment: ' + errors.slice(0, 3).join('; '));
  process.exit(1);
}
const clean = sanitizeSemanticFragment(raw);
const saved = saveSemanticCache(clean.nodes || [], clean.edges || [], clean.hyperedges || []);
console.log(\`Cached ${saved} files\`);
"

Merge cached + new results into .graphify/.graphify_semantic.json. Each chunk is independently validated; invalid chunks are skipped with a warning rather than crashing the merge (mirrors upstream PR #825):

node -e "
const fs = require('fs');
const { validateSemanticFragment, sanitizeSemanticFragment } = require('@sentropic/graphify');

function loadAndClean(path, label) {
  if (!fs.existsSync(path)) return {nodes:[],edges:[],hyperedges:[]};
  let raw;
  try { raw = JSON.parse(fs.readFileSync(path, 'utf-8')); }
  catch (e) { console.warn(\`Skipping invalid \${label} fragment \${path}: \${e.message}\`); return {nodes:[],edges:[],hyperedges:[]}; }
  const errs = validateSemanticFragment(raw);
  if (errs.length > 0) { console.warn(\`Skipping invalid \${label} fragment \${path}: \` + errs.slice(0, 3).join('; ')); return {nodes:[],edges:[],hyperedges:[]}; }
  return sanitizeSemanticFragment(raw);
}

const cached = loadAndClean('.graphify/.graphify_cached.json', 'cached');
const raw = loadAndClean('.graphify/.graphify_semantic_new.json', 'new');

const allNodes = (cached.nodes || []).concat(raw.nodes || []);
const allEdges = (cached.edges || []).concat(raw.edges || []);
const allHyperedges = (cached.hyperedges || []).concat(raw.hyperedges || []);
const seen = new Set();
const deduped = [];
for (const n of allNodes) { if (!seen.has(n.id)) { seen.add(n.id); deduped.push(n); } }

const merged = {
    nodes: deduped,
    edges: allEdges,
    hyperedges: allHyperedges,
    input_tokens: raw.input_tokens || 0,
    output_tokens: raw.output_tokens || 0,
};
fs.writeFileSync('.graphify/.graphify_semantic.json', JSON.stringify(merged, null, 2));
console.log(\`Extraction complete - ${deduped.length} nodes, ${allEdges.length} edges (${(cached.nodes||[]).length} from cache, ${(raw.nodes||[]).length} new)\`);
"

Clean up temp files: Remove-Item -ErrorAction SilentlyContinue .graphify/.graphify_cached.json, .graphify/.graphify_uncached.txt, .graphify/.graphify_semantic_new.json, .graphify/.graphify_detect_semantic.json, .graphify/.graphify_transcripts.json, .graphify/.graphify_pdf_ocr.json

Part C - Merge AST + semantic into final extraction

The AST side comes from Tree-sitter and is trusted as-is. The semantic side is LLM-generated and must be sanitized one more time before the final merge:

node -e "
const fs = require('fs');
const { sanitizeSemanticFragment } = require('@sentropic/graphify');

const ast = JSON.parse(fs.readFileSync('.graphify/.graphify_ast.json', 'utf-8'));
const sem = sanitizeSemanticFragment(JSON.parse(fs.readFileSync('.graphify/.graphify_semantic.json', 'utf-8')));

const seen = new Set(ast.nodes.map(n => n.id));
const mergedNodes = [...ast.nodes];
for (const n of sem.nodes) { if (!seen.has(n.id)) { mergedNodes.push(n); seen.add(n.id); } }

const mergedEdges = ast.edges.concat(sem.edges);
const mergedHyperedges = sem.hyperedges || [];
const merged = {
    nodes: mergedNodes,
    edges: mergedEdges,
    hyperedges: mergedHyperedges,
    input_tokens: sem.input_tokens || 0,
    output_tokens: sem.output_tokens || 0,
};
fs.writeFileSync('.graphify/.graphify_extract.json', JSON.stringify(merged, null, 2));
console.log(\`Merged: ${mergedNodes.length} nodes, ${mergedEdges.length} edges (${ast.nodes.length} AST + ${sem.nodes.length} semantic)\`);
"

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

New-Item -ItemType Directory -Force -Path .graphify | Out-Null
node -e "
const fs = require('fs');
const { buildFromJson } = require('@sentropic/graphify');
const { cluster, scoreAll } = require('@sentropic/graphify');
const { godNodes, surprisingConnections, suggestQuestions } = require('@sentropic/graphify');
const { generateReport } = require('@sentropic/graphify');
const { toJson } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const detection = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));

const G = buildFromJson(extraction);
const communities = cluster(G);
const cohesion = scoreAll(G, communities);
const tokens = {input: extraction.input_tokens || 0, output: extraction.output_tokens || 0};
const gods = godNodes(G);
const surprises = surprisingConnections(G, communities);
const labels = new Map(Array.from(communities.keys(), cid => [cid, 'Community ' + cid]));
const questions = suggestQuestions(G, communities, labels);

const report = generateReport(G, communities, cohesion, labels, gods, surprises, detection, tokens, '.', {suggestedQuestions: questions});
fs.writeFileSync('.graphify/GRAPH_REPORT.md', report);
toJson(G, communities, '.graphify/graph.json');

const analysis = {
    communities: Object.fromEntries(Array.from(communities.entries(), ([k, v]) => [String(k), v])),
    cohesion: Object.fromEntries(Array.from(cohesion.entries(), ([k, v]) => [String(k), v])),
    gods,
    surprises,
    questions,
};
fs.writeFileSync('.graphify/.graphify_analysis.json', JSON.stringify(analysis, null, 2));
if (G.order === 0) {
    console.log('ERROR: Graph is empty - extraction produced no nodes.');
    console.log('Possible causes: all files were skipped, binary-only corpus, or extraction failed.');
    process.exit(1);
}
console.log(\`Graph: ${G.order} nodes, ${G.size} edges, ${communities.size} communities\`);
"

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 5 - Label communities

Read .graphify/.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").

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

node -e "
const fs = require('fs');
const { buildFromJson } = require('@sentropic/graphify');
const { scoreAll } = require('@sentropic/graphify');
const { godNodes, surprisingConnections, suggestQuestions } = require('@sentropic/graphify');
const { generateReport } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const detection = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));

const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));
const cohesion = Object.fromEntries(Object.entries(analysis.cohesion).map(([k, v]) => [Number(k), v]));
const tokens = {input: extraction.input_tokens || 0, output: extraction.output_tokens || 0};

// LABELS - replace these with the names you chose above
const labels = LABELS_DICT;

const questions = suggestQuestions(G, communities, labels);

const report = generateReport(G, communities, cohesion, labels, analysis.gods, analysis.surprises, detection, tokens, '.', {suggestedQuestions: questions});
fs.writeFileSync('.graphify/GRAPH_REPORT.md', report);
fs.writeFileSync('.graphify/.graphify_labels.json', JSON.stringify(Object.fromEntries(Object.entries(labels).map(([k, v]) => [String(k), v]))));
console.log('Report updated with community labels');
"

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.

Wiki descriptions are opt-in and two-step. First generate sidecars, then pass their index into wiki or Obsidian rendering:

graphify wiki describe --graph .graphify/graph.json --mode assistant --targets all
# or, for direct mode:
graphify wiki describe --graph .graphify/graph.json --mode direct --backend openai --targets all

graphify export wiki --graph .graphify/graph.json --descriptions .graphify/wiki/descriptions.json
graphify export obsidian --graph .graphify/graph.json --descriptions .graphify/wiki/descriptions.json

Sidecars live under .graphify/wiki/descriptions/ with an index at .graphify/wiki/descriptions.json. They record graph hash, prompt/generator provenance, evidence refs, and cache keys; existing generated sidecars may be reused when a fresh generation does not complete. insufficient_evidence sidecars render no Description section. This never mutates .graphify/graph.json.

If --obsidian was given:

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

node -e "
const fs = require('fs');
const { buildFromJson } = require('@sentropic/graphify');
const { toWiki, toCanvas } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));
const labelsRaw = fs.existsSync('.graphify/.graphify_labels.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_labels.json', 'utf-8')) : {};

const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));
const cohesion = Object.fromEntries(Object.entries(analysis.cohesion).map(([k, v]) => [Number(k), v]));
const labels = Object.fromEntries(Object.entries(labelsRaw).map(([k, v]) => [Number(k), v]));

const obsidianDir = 'OBSIDIAN_DIR';  // replace with --obsidian-dir value, or '.graphify/obsidian' if not given

const n = toWiki(G, communities, obsidianDir, {communityLabels: Object.keys(labels).length ? labels : undefined, cohesion});
console.log(\`Obsidian vault: ${n} notes in ${obsidianDir}/\`);

toCanvas(G, communities, \`${obsidianDir}/graph.canvas\`, {communityLabels: Object.keys(labels).length ? labels : undefined});
console.log(\`Canvas: ${obsidianDir}/graph.canvas - open in Obsidian for structured community layout\`);
console.log();
console.log(\`Open ${obsidianDir}/ as a vault in Obsidian.\`);
console.log('  Graph view   - nodes colored by community (set automatically)');
console.log('  graph.canvas - structured layout with communities as groups');
console.log('  _COMMUNITY_* - overview notes with cohesion scores and dataview queries');
"

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

node -e "
const fs = require('fs');
const { buildFromJson } = require('@sentropic/graphify');
const { toHtml } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));
const labelsRaw = fs.existsSync('.graphify/.graphify_labels.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_labels.json', 'utf-8')) : {};

const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));
const labels = Object.fromEntries(Object.entries(labelsRaw).map(([k, v]) => [Number(k), v]));

if (G.order > 5000) {
    console.log(\`Graph has ${G.order} nodes - too large for HTML viz. Use Obsidian vault instead.\`);
} else {
    toHtml(G, communities, '.graphify/graph.html', {communityLabels: Object.keys(labels).length ? labels : undefined});
    console.log('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:

node -e "
const fs = require('fs');
const { buildFromJson, toCypher } = require('@sentropic/graphify');

const G = buildFromJson(JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8')));
toCypher(G, '.graphify/cypher.txt');
console.log('cypher.txt written - import with: cypher-shell < .graphify/cypher.txt');
"

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

node -e "
const fs = require('fs');
const { buildFromJson, pushToNeo4j } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));
const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));

const result = pushToNeo4j(G, {uri: 'NEO4J_URI', user: 'NEO4J_USER', password: 'NEO4J_PASSWORD', communities});
console.log(\`Pushed to Neo4j: ${result.nodes} nodes, ${result.edges} edges\`);
"

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.

Step 7b - SVG export (only if --svg flag)

node -e "
const fs = require('fs');
const { buildFromJson, toSvg } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));
const labelsRaw = fs.existsSync('.graphify/.graphify_labels.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_labels.json', 'utf-8')) : {};

const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));
const labels = Object.fromEntries(Object.entries(labelsRaw).map(([k, v]) => [Number(k), v]));

toSvg(G, communities, '.graphify/graph.svg', {communityLabels: Object.keys(labels).length ? labels : undefined});
console.log('graph.svg written - embeds in Obsidian, Notion, GitHub READMEs');
"

Step 7c - GraphML export (only if --graphml flag)

node -e "
const fs = require('fs');
const { buildFromJson, toGraphml } = require('@sentropic/graphify');

const extraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const analysis = JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'));

const G = buildFromJson(extraction);
const communities = Object.fromEntries(Object.entries(analysis.communities).map(([k, v]) => [Number(k), v]));

toGraphml(G, communities, '.graphify/graph.graphml');
console.log('graph.graphml written - open in Gephi, yEd, or any GraphML tool');
"

Step 7d - MCP server (only if --mcp flag)

npx graphify serve .graphify/graph.json

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.

To configure in Claude Desktop, add to claude_desktop_config.json:

{
  "mcpServers": {
    "graphify": {
      "command": "npx",
      "args": ["graphify", "serve", "/absolute/path/to/.graphify/graph.json"]
    }
  }
}

Step 8 - Token reduction benchmark (only if total_words > 5000)

If total_words from .graphify/.graphify_detect.json is greater than 5,000, run:

node -e "
const fs = require('fs');
const { runBenchmark, printBenchmark } = require('@sentropic/graphify');

const detection = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));
const result = runBenchmark('.graphify/graph.json', {corpusWords: detection.total_words});
printBenchmark(result);
"

Print the output directly in chat. If total_words <= 5000, skip silently - the graph value is structural clarity, not token compression, for small corpora.

Step 9 - Save manifest, update cost tracker, clean up, and report

node -e "
const fs = require('fs');
const { saveManifest } = require('@sentropic/graphify');

const detect = JSON.parse(fs.readFileSync('.graphify/.graphify_detect.json', 'utf-8'));
saveManifest(detect.files);

const extract = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const inputTok = extract.input_tokens || 0;
const outputTok = extract.output_tokens || 0;

const costPath = '.graphify/cost.json';
const cost = fs.existsSync(costPath) ? JSON.parse(fs.readFileSync(costPath, 'utf-8')) : {runs: [], total_input_tokens: 0, total_output_tokens: 0};

cost.runs.push({
    date: new Date().toISOString(),
    input_tokens: inputTok,
    output_tokens: outputTok,
    files: detect.total_files || 0,
});
cost.total_input_tokens += inputTok;
cost.total_output_tokens += outputTok;
fs.writeFileSync(costPath, JSON.stringify(cost, null, 2));

console.log(\`This run: \${inputTok.toLocaleString()} input tokens, \${outputTok.toLocaleString()} output tokens\`);
console.log(\`All time: \${cost.total_input_tokens.toLocaleString()} input, \${cost.total_output_tokens.toLocaleString()} output (\${cost.runs.length} runs)\`);
"
Remove-Item -ErrorAction SilentlyContinue .graphify/.graphify_detect.json, .graphify/.graphify_extract.json, .graphify/.graphify_ast.json, .graphify/.graphify_semantic.json, .graphify/.graphify_analysis.json
Remove-Item -ErrorAction SilentlyContinue .graphify/needs_update

Tell the user (omit the obsidian line unless --obsidian was given):

Graph complete. Outputs in PATH_TO_DIR/.graphify/

  graph.html            - interactive graph, open in browser
  GRAPH_REPORT.md       - audit report
  graph.json            - raw graph data
  obsidian/             - Obsidian vault (only if --obsidian was given)

Replace PATH_TO_DIR with the actual absolute path of the directory that was processed.

Then paste these sections from GRAPH_REPORT.md directly into the chat:

God Nodes
Surprising Connections
Suggested Questions

Do NOT paste the full report - just those three sections. Keep it concise.

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:

"The most interesting question this graph can answer: [question]. Want me to trace it?"

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.

The graph is the map. Your job after the pipeline is to be the guide.

For --update (incremental re-extraction)

Use when you've added or modified files since the last run. Only re-extracts changed files - saves tokens and time.

node -e "
const fs = require('fs');
const { detectIncremental } = require('@sentropic/graphify');

const result = detectIncremental('INPUT_PATH');
const newTotal = result.new_total || 0;
console.log(JSON.stringify(result, null, 2));
fs.writeFileSync('.graphify/.graphify_incremental.json', JSON.stringify(result));
if (newTotal === 0) {
    console.log('No files changed since last run. Nothing to update.');
    process.exit(0);
}
console.log(\`${newTotal} new/changed file(s) to re-extract.\`);
"

If new files exist, first check whether all changed files are code files:

node -e "
const fs = require('fs');
const path = require('path');

const result = fs.existsSync('.graphify/.graphify_incremental.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_incremental.json', 'utf-8')) : {};
const codeExts = new Set(['.py','.ts','.js','.go','.rs','.java','.cpp','.c','.rb','.swift','.kt','.cs','.scala','.php','.cc','.cxx','.hpp','.h','.kts','.lua','.toc']);
const newFiles = result.new_files || {};
const allChanged = Object.values(newFiles).flat();
const codeOnly = allChanged.every(f => codeExts.has(path.extname(f).toLowerCase()));
console.log('code_only:', codeOnly);
"

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.

If code_only is False (any changed file is a doc/paper/image/video): first prepare transcripts and PDF sidecars if needed, then run the full Steps 3A–3C pipeline as normal.

When code_only is False, run this before Step 3B:

node -e "
(async () => {
const fs = require('fs');
const { prepareSemanticDetection } = require('@sentropic/graphify');

const detect = JSON.parse(fs.readFileSync('.graphify/.graphify_incremental.json', 'utf-8'));
const analysis = fs.existsSync('.graphify/.graphify_analysis.json')
  ? JSON.parse(fs.readFileSync('.graphify/.graphify_analysis.json', 'utf-8'))
  : null;

const { detection: semanticDetect, transcriptPaths, pdfArtifacts } = await prepareSemanticDetection(detect, {
  transcriptOutputDir: '.graphify/transcripts',
  pdfOutputDir: '.graphify/converted/pdf',
  godNodes: (analysis && analysis.gods) || [],
  incremental: true,
});

fs.writeFileSync('.graphify/.graphify_incremental_semantic.json', JSON.stringify(semanticDetect, null, 2));
fs.writeFileSync('.graphify/.graphify_transcripts.json', JSON.stringify(transcriptPaths, null, 2));
fs.writeFileSync('.graphify/.graphify_pdf_ocr.json', JSON.stringify(pdfArtifacts, null, 2));
console.log('Prepared semantic inputs: ' + transcriptPaths.length + ' transcript(s), ' + pdfArtifacts.filter((item) => item.markdownPath).length + ' PDF sidecar(s)');
)().catch((error) => {
  console.error(error instanceof Error ? error.message : String(error));
  process.exit(1);
});
"

When re-running Step 3B in update mode, use .graphify/.graphify_incremental_semantic.json instead of .graphify/.graphify_detect_semantic.json.

Then:

node -e "
const fs = require('fs');
const Graph = require('graphology');
const { buildFromJson } = require('@sentropic/graphify');

// Load existing graph
const existingData = JSON.parse(fs.readFileSync('.graphify/graph.json', 'utf-8'));
const GExisting = new Graph({type: 'undirected'});
for (const n of existingData.nodes) { const {id, ...a} = n; GExisting.mergeNode(id, a); }
for (const l of existingData.links) { const {source, target, ...a} = l; if (GExisting.hasNode(source) && GExisting.hasNode(target)) try { GExisting.mergeEdge(source, target, a); } catch {} }

// Load new extraction
const newExtraction = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const GNew = buildFromJson(newExtraction);

// Merge: new nodes/edges into existing graph
GNew.forEachNode((n, a) => GExisting.mergeNode(n, a));
GNew.forEachEdge((e, a, s, t) => { try { GExisting.mergeEdge(s, t, a); } catch {} });
console.log(\`Merged: ${GExisting.order} nodes, ${GExisting.size} edges\`);
"

Then run Steps 4–8 on the merged graph as normal.

After Step 4, show the graph diff:

node -e "
const fs = require('fs');
const Graph = require('graphology');
const { graphDiff, buildFromJson } = require('@sentropic/graphify');

const oldData = fs.existsSync('.graphify/.graphify_old.json') ? JSON.parse(fs.readFileSync('.graphify/.graphify_old.json', 'utf-8')) : null;
const newExtract = JSON.parse(fs.readFileSync('.graphify/.graphify_extract.json', 'utf-8'));
const GNew = buildFromJson(newExtract);

if (oldData) {
    const GOld = new Graph({type: 'undirected'});
    for (const n of oldData.nodes) { const {id, ...a} = n; GOld.mergeNode(id, a); }
    for (const l of oldData.links) { const {source, target, ...a} = l; if (GOld.hasNode(source) && GOld.hasNode(target)) try { GOld.mergeEdge(source, target, a); } catch {} }
    const diff = graphDiff(GOld, GNew);
    console.log(diff.summary);
    if (diff.new_nodes && diff.new_nodes.length) {
        console.log('New nodes:', diff.new_nodes.slice(0, 5).map(n => n.label).join(', '));
    }
    if (diff.new_edges && diff.new_edges.length) {
        console.log('New edges:', diff.new_edges.length);
    }
}
"

Before the merge step, save the old graph: Copy-Item .graphify\graph.json .graphify/.graphify_old.json Clean up after: Remove-Item -ErrorAction SilentlyContinue .graphify/.graphify_old.json

For --cluster-only

Skip Steps 1–3. Load the existing graph from .graphify/graph.json and re-run clustering:

node -e "
const fs = require('fs');
const Graph = require('graphology');
const { cluster, scoreAll } = require('@sentropic/graphify');
const { godNodes, surprisingConnections } = require('@sentropic/graphify');
const { generateReport } = require('@sentropic/graphify');
const { toJson } = require('@sentropic/graphify');

const data = JSON.parse(fs.readFileSync('.graphify/graph.json', 'utf-8'));
const G = new Graph({type: 'undirected'});
for (const n of data.nodes) { const {id, ...a} = n; G.mergeNode(id, a); }
for (const l of data.links) { const {source, target, ...a} = l; if (G.hasNode(source) && G.hasNode(target)) try { G.mergeEdge(source, target, a); } catch {} }

const detection = {total_files: 0, total_words: 99999, needs_graph: true, warning: null,
             files: {code: [], document: [], paper: []}};
const tokens = {input: 0, output: 0};

const communities = cluster(G);
const cohesion = scoreAll(G, communities);
const gods = godNodes(G);
const surprises = surprisingConnections(G, communities);
const labels = new Map(Array.from(communities.keys(), cid => [cid, 'Community ' + cid]));

const report = generateReport(G, communities, cohesion, labels, gods, surprises, detection, tokens, '.');
fs.writeFileSync('.graphify/GRAPH_REPORT.md', report);
toJson(G, communities, '.graphify/graph.json');

const analysis = {
    communities: Object.fromEntries(Array.from(communities.entries(), ([k, v]) => [String(k), v])),
    cohesion: Object.fromEntries(Array.from(cohesion.entries(), ([k, v]) => [String(k), v])),
    gods,
    surprises,
};
fs.writeFileSync('.graphify/.graphify_analysis.json', JSON.stringify(analysis, null, 2));
console.log(\`Re-clustered: ${communities.size} communities\`);
"

Then run Steps 5–9 as normal (label communities, generate viz, benchmark, clean up, report).

For /graphify query

Two traversal modes - choose based on the question:

Mode	Flag	Best for
BFS (default)	(none)	"What is X connected to?" - broad context, nearest neighbors first
DFS	`--dfs`	"How does X reach Y?" - trace a specific chain or dependency path

First check the graph exists:

node -e "
const fs = require('fs');
if (!fs.existsSync('.graphify/graph.json')) {
    console.log('ERROR: No graph found. Run /graphify <path> first to build the graph.');
    process.exit(1);
}
"

If it fails, stop and tell the user to run /graphify <path> first.

Step 0 - Constrained query expansion (before traversal)

graphify query matches nodes by case-folded substring + IDF - no stemming, no synonyms, no cross-language match. When the question uses different vocabulary than the graph labels (user says "обработчик" / graph says "handler"; "authentication" / "Guardian"), the literal matcher returns 0 hits. Expand the query against the actual graph vocabulary first - never invent tokens:

node -e "
const fs = require('fs');
const g = JSON.parse(fs.readFileSync('.graphify/graph.json','utf-8'));
const vocab = new Set();
for (const n of (g.nodes || [])) {
  for (const c of String(n.label || '').match(/[^\W\d_]+/gu) || []) {
    for (const p of (c.match(/[A-Z]+(?=[A-Z][a-z])|[A-Z]?[a-z]+|[A-Z]+/g) || [c])) {
      if (p.length > 1) vocab.add(p.toLowerCase());
    }
  }
}
fs.writeFileSync('.graphify/.vocab.txt', [...vocab].sort().join('\n'));
console.log('vocab: ' + vocab.size + ' tokens');
"

Read .graphify/.vocab.txt, then pick up to 12 tokens from that exact list that match the query intent. Hard constraints:

Use only tokens present in .vocab.txt - do not invent tokens.
A concept with no plausible vocab token: skip it, no near-synonym from memory.
No vocab token matches at all: output an empty list and tell the user the corpus has no relevant vocabulary; do not fabricate a search.
Cross-language: e.g. Russian "аутентификация" - look for auth, credential, token, security iff present in the vocab.

Print the selection before querying (Query expanded to (from graph vocab, N tokens): [...]), then run graphify query with the joined expanded tokens (keep the original question only for save-result).

Before deep traversal, run the compact first-hop summary and use it to choose the right graph action:

graphify summary --graph .graphify/graph.json
graphify recommend-commits --files src/auth.ts,src/session.ts --graph .graphify/graph.json
graphify review-analysis --files src/auth.ts --graph .graphify/graph.json
graphify review-eval --cases .graphify/review-cases.json --graph .graphify/graph.json

Load .graphify/graph.json, then:

Find the 1-3 nodes whose label best matches key terms in the question.
Run the appropriate traversal from each starting node.
Read the subgraph - node labels, edge relations, confidence tags, source locations.
Answer using only what the graph contains. Quote source_location when citing a specific fact.
If the graph lacks enough information, say so - do not hallucinate edges.

node -e "
const fs = require('fs');
const Graph = require('graphology');

const data = JSON.parse(fs.readFileSync('.graphify/graph.json', 'utf-8'));
const G = new Graph({type: 'undirected'});
for (const n of data.nodes) { const {id, ...a} = n; G.mergeNode(id, a); }
for (const l of data.links) { const {source, target, ...a} = l; if (G.hasNode(source) && G.hasNode(target)) try { G.mergeEdge(source, target, a); } catch {} }

const question = 'QUESTION';
const mode = 'MODE';
const terms = question.split(/\s+/).filter(t => t.length > 3).map(t => t.toLowerCase());

const scored = [];
G.forEachNode((nid, ndata) => {
    const label = (ndata.label || '').toLowerCase();
    const score = terms.filter(t => label.includes(t)).length;
    if (score > 0) scored.push([score, nid]);
});
scored.sort((a, b) => b[0] - a[0]);
const startNodes = scored.slice(0, 3).map(s => s[1]);

if (!startNodes.length) { console.log('No matching nodes found for query terms:', terms); process.exit(0); }

const subgraphNodes = new Set();
const subgraphEdges = [];

if (mode === 'dfs') {
    const visited = new Set();
    const stack = [...startNodes].reverse().map(n => [n, 0]);
    while (stack.length) {
        const [node, depth] = stack.pop();
        if (visited.has(node) || depth > 6) continue;
        visited.add(node); subgraphNodes.add(node);
        G.forEachNeighbor(node, neighbor => { if (!visited.has(neighbor)) { stack.push([neighbor, depth + 1]); subgraphEdges.push([node, neighbor]); } });
    }
} else {
    let frontier = new Set(startNodes);
    startNodes.forEach(n => subgraphNodes.add(n));
    for (let i = 0; i < 3; i++) {
        const nextFrontier = new Set();
        for (const n of frontier) { G.forEachNeighbor(n, neighbor => { if (!subgraphNodes.has(neighbor)) { nextFrontier.add(neighbor); subgraphEdges.push([n, neighbor]); } }); }
        nextFrontier.forEach(n => subgraphNodes.add(n));
        frontier = nextFrontier;
    }
}

const tokenBudget = BUDGET;
const charBudget = tokenBudget * 4;
const relevance = nid => { const label = (G.getNodeAttributes(nid).label || '').toLowerCase(); return terms.filter(t => label.includes(t)).length; };
const rankedNodes = [...subgraphNodes].sort((a, b) => relevance(b) - relevance(a));

const lines = [\`Traversal: \${mode.toUpperCase()} | Start: \${JSON.stringify(startNodes.map(n => G.getNodeAttribute(n, 'label') || n))} | \${subgraphNodes.size} nodes\`];
for (const nid of rankedNodes) { const d = G.getNodeAttributes(nid); lines.push(\`  NODE \${d.label || nid} [src=\${d.source_file || ''} loc=\${d.source_location || ''}]\`); }
for (const [u, v] of subgraphEdges) { if (subgraphNodes.has(u) && subgraphNodes.has(v)) { const edge = G.hasEdge(u, v) ? G.getEdgeAttributes(G.edge(u, v)) : {}; lines.push(\`  EDGE \${G.getNodeAttribute(u, 'label') || u} --\${edge.relation || ''} [\${edge.confidence || ''}]--> \${G.getNodeAttribute(v, 'label') || v}\`); } }

let output = lines.join('
');
if (output.length > charBudget) { output = output.slice(0, charBudget) + \`
... (truncated at ~\${tokenBudget} token budget - use --budget N for more)\`; }
console.log(output);
"

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.

After writing the answer, save it back into the graph so it improves future queries:

node -e "
const { saveQueryResult } = require('@sentropic/graphify');
saveQueryResult({
    question: 'QUESTION',
    answer: 'ANSWER',
    memoryDir: '.graphify/memory',
    queryType: 'query',
    sourceNodes: SOURCE_NODES,  // list of node labels cited, or []
});
console.log('Query result saved to .graphify/memory/');
"

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.

For /graphify path

Find the shortest path between two named concepts in the graph.

First check the graph exists:

node -e "
const fs = require('fs');
if (!fs.existsSync('.graphify/graph.json')) {
    console.log('ERROR: No graph found. Run /graphify <path> first to build the graph.');
    process.exit(1);
}
"

If it fails, stop and tell the user to run /graphify <path> first.

node -e "
const fs = require('fs');
const Graph = require('graphology');
const { bidirectional } = require('graphology-shortest-path/unweighted');

const data = JSON.parse(fs.readFileSync('.graphify/graph.json', 'utf-8'));
const G = new Graph({type: 'undirected'});
for (const n of data.nodes) { const {id, ...a} = n; G.mergeNode(id, a); }
for (const l of data.links) { const {source, target, ...a} = l; if (G.hasNode(source) && G.hasNode(target)) try { G.mergeEdge(source, target, a); } catch {} }

const aTerm = 'NODE_A';
const bTerm = 'NODE_B';

function findNode(term) {
    term = term.toLowerCase();
    const scored = [];
    G.forEachNode((n, a) => {
        const label = (a.label || '').toLowerCase();
        const score = term.split(/\s+/).filter(w => label.includes(w)).length;
        if (score > 0) scored.push([score, n]);
    });
    scored.sort((a, b) => b[0] - a[0]);
    return scored.length && scored[0][0] > 0 ? scored[0][1] : null;
}

const src = findNode(aTerm);
const tgt = findNode(bTerm);

if (!src || !tgt) {
    console.log(\`Could not find nodes matching: '${aTerm}' or '${bTerm}'\`);
    process.exit(0);
}

const path = bidirectional(G, src, tgt);
if (!path) {
    console.log(\`No path found between '${aTerm}' and '${bTerm}'\`);
} else {
    console.log(\`Shortest path (${path.length - 1} hops):\`);
    for (let i = 0; i < path.length; i++) {
        const nid = path[i];
        const label = G.getNodeAttribute(nid, 'label') || nid;
        if (i < path.length - 1) {
            const edgeKey = G.edge(nid, path[i + 1]);
            const edge = edgeKey ? G.getEdgeAttributes(edgeKey) : {};
            console.log(\`  ${label} --${edge.relation || ''}--> [${edge.confidence || ''}]\`);
        } else {
            console.log(\`  ${label}\`);
        }
    }
}
"

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.

After writing the explanation, save it back:

node -e "
const { saveQueryResult } = require('@sentropic/graphify');
saveQueryResult({
    question: 'Path from NODE_A to NODE_B',
    answer: 'ANSWER',
    memoryDir: '.graphify/memory',
    queryType: 'path_query',
    sourceNodes: PATH_NODES,  // list of node labels on the path
});
console.log('Path result saved to .graphify/memory/');
"

For /graphify explain

Give a plain-language explanation of a single node - everything connected to it.

First check the graph exists:

node -e "
const fs = require('fs');
if (!fs.existsSync('.graphify/graph.json')) {
    console.log('ERROR: No graph found. Run /graphify <path> first to build the graph.');
    process.exit(1);
}
"

If it fails, stop and tell the user to run /graphify <path> first.

node -e "
const fs = require('fs');
const Graph = require('graphology');

const data = JSON.parse(fs.readFileSync('.graphify/graph.json', 'utf-8'));
const G = new Graph({type: 'undirected'});
for (const n of data.nodes) { const {id, ...a} = n; G.mergeNode(id, a); }
for (const l of data.links) { const {source, target, ...a} = l; if (G.hasNode(source) && G.hasNode(target)) try { G.mergeEdge(source, target, a); } catch {} }

const term = 'NODE_NAME';
const termLower = term.toLowerCase();

const scored = [];
G.forEachNode((n, a) => { const label = (a.label || '').toLowerCase(); const score = termLower.split(/\s+/).filter(w => label.includes(w)).length; if (score > 0) scored.push([score, n]); });
scored.sort((a, b) => b[0] - a[0]);
if (!scored.length || scored[0][0] === 0) { console.log(\`No node matching '\${term}'\`); process.exit(0); }

const nid = scored[0][1];
const dataN = G.getNodeAttributes(nid);
console.log(\`NODE: \${dataN.label || nid}\`);
console.log(\`  source: \${dataN.source_file || 'unknown'}\`);
console.log(\`  type: \${dataN.file_type || 'unknown'}\`);
console.log(\`  degree: \${G.degree(nid)}\`);
console.log();
console.log('CONNECTIONS:');
G.forEachNeighbor(nid, (neighbor) => {
    const ek = G.edge(nid, neighbor); const edge = ek ? G.getEdgeAttributes(ek) : {};
    const nlabel = G.getNodeAttribute(neighbor, 'label') || neighbor;
    console.log(\`  --\${edge.relation || ''}--> \${nlabel} [\${edge.confidence || ''}] (\${G.getNodeAttribute(neighbor, 'source_file') || ''})\`);
});
"

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.

After writing the explanation, save it back:

node -e "
const { saveQueryResult } = require('@sentropic/graphify');
saveQueryResult({
    question: 'Explain NODE_NAME',
    answer: 'ANSWER',
    memoryDir: '.graphify/memory',
    queryType: 'explain',
    sourceNodes: ['NODE_NAME'],
});
console.log('Explanation saved to .graphify/memory/');
"

For /graphify add

Fetch a URL and add it to the corpus, then update the graph.

node -e "
const { ingest } = require('@sentropic/graphify');
try {
    const out = ingest('URL', './raw', {author: 'AUTHOR', contributor: 'CONTRIBUTOR'});
    console.log(\`Saved to ${out}\`);
} catch (e) {
    console.error(\`error: ${e.message}\`);
    process.exit(1);
}
"

Replace URL with the actual URL, AUTHOR with the user's name if provided, CONTRIBUTOR likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the --update pipeline on ./raw to merge the new file into the existing graph.

Supported URL types (auto-detected):

Twitter/X → fetched via oEmbed, saved as .md with tweet text and author
arXiv → abstract + metadata saved as .md
YouTube / video URLs → audio downloaded locally via yt-dlp; transcript generated on the next build/update (requires local yt-dlp, ffmpeg, and faster-whisper-ts)
PDF → downloaded as .pdf
Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
Any webpage → converted to markdown via html2text

For --watch

Start a background watcher that monitors a folder and auto-updates the graph when files change.


*Truncated - read the full file at https://github.com/coderXdevelop/chatapp/blob/e6d46d07de224081f76e5f31938dbd73a39e4680/.claude/skills/graphify/SKILL.md.*

graphify-windows

About

/graphify

Usage

What graphify is for

What You Must Do When Invoked

Step 1 - Ensure graphify is installed

Step 2 - Detect files

Step 2.5 - Prepare semantic detection, including audio/video transcripts and PDF preflight/OCR when needed

Step 3 - Extract entities and relationships

Part A - Structural extraction for code files

Part B - Semantic extraction (parallel subagents)

Part C - Merge AST + semantic into final extraction

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

Step 5 - Label communities

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

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

Step 7b - SVG export (only if --svg flag)

Step 7c - GraphML export (only if --graphml flag)

Step 7d - MCP server (only if --mcp flag)

Step 8 - Token reduction benchmark (only if total_words > 5000)

Step 9 - Save manifest, update cost tracker, clean up, and report

For --update (incremental re-extraction)

For --cluster-only

For /graphify query

Step 0 - Constrained query expansion (before traversal)

For /graphify path

For /graphify explain

For /graphify add

For --watch

Use it

Manifest

Reviews