/graphify                                             # full pipeline on current directory → Obsidian vault
/graphify <path>                                      # full pipeline on specific path
/graphify https://github.com/<owner>/<repo>           # clone repo then run full pipeline on it
/graphify https://github.com/<owner>/<repo> --branch <branch>  # clone a specific branch
/graphify <url1> <url2> ...                           # clone multiple repos, build each, merge into one cross-repo graph
/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
/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

Drop any folder of code, docs, papers, images, or video into graphify and get a queryable knowledge graph. Persistent across sessions, honest audit trail (EXTRACTED/INFERRED/AMBIGUOUS), community detection surfaces cross-document connections you wouldn't think to ask about.

What You Must Do When Invoked

If the user invoked /graphify --help or /graphify -h (with no other arguments), print the contents of the ## Usage section above verbatim and stop. Do not run any commands, do not detect files, do not default the path to .. Just print the Usage block and return.

Fast path — existing graph: Before doing anything else, check whether graphify-out/graph.json exists. The expected location is graphify-out/graph.json relative to the current working directory (i.e. the project root where you are running commands). If it exists AND the user's request is a natural-language question about the codebase (e.g. "How does X work?", "What calls Y?", "Trace the data flow through Z") and NOT an explicit rebuild command (--update, --cluster-only, or a bare path/URL that implies fresh extraction): skip Steps 1–5 entirely and jump straight to ## For /graphify query. Run graphify query "<question>" immediately. Do not run detect. Do not check corpus size. Do not ask the user to narrow. The graph is already built — use it.

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

If the path argument starts with https://github.com/ or http://github.com/, treat it as a GitHub URL - run Step 0 before anything else, then continue with the resolved local path.

Follow these steps in order. Do not skip steps.

Step 0 - Clone GitHub repo(s) (only if a GitHub URL was given)

Single repo:

LOCAL_PATH=$(graphify clone <github-url> [--branch <branch>])
# Use LOCAL_PATH as the target for all subsequent steps

Multiple repos (cross-repo graph):

# Clone each repo, run the full pipeline on each, then merge
graphify clone <url1>   # → ~/.graphify/repos/<owner1>/<repo1>
graphify clone <url2>   # → ~/.graphify/repos/<owner2>/<repo2>
# Run /graphify on each local path to produce their graph.json files
# Then merge:
graphify merge-graphs \
  ~/.graphify/repos/<owner1>/<repo1>/graphify-out/graph.json \
  ~/.graphify/repos/<owner2>/<repo2>/graphify-out/graph.json \
  --out graphify-out/cross-repo-graph.json

Graphify clones into ~/.graphify/repos/<owner>/<repo> and reuses existing clones on repeat runs. Each node in the merged graph carries a repo attribute so you can filter by origin.

Multiple local subfolders (monorepo or multi-service layout):

The skill pipeline writes all intermediate and final outputs to graphify-out/ in the current working directory. Running the skill on each subfolder separately will clobber the same output dir. Instead, use the CLI directly for each subfolder — it places graphify-out/ inside the scanned path:

graphify extract ./core/     # → ./core/graphify-out/graph.json
graphify extract ./service/  # → ./service/graphify-out/graph.json
graphify extract ./platform/ # → ./platform/graphify-out/graph.json
# Add --backend gemini|kimi|openai|deepseek|claude-cli depending on which API key you have set

# Then merge at the project root:
graphify merge-graphs \
  ./core/graphify-out/graph.json \
  ./service/graphify-out/graph.json \
  ./platform/graphify-out/graph.json \
  --out graphify-out/graph.json

Once graphify-out/graph.json exists, the fast path above takes over: any codebase question runs graphify query directly on the merged graph — no re-extraction, no size gate.

Step 1 - Ensure graphify is installed

# Detect the correct Python interpreter (handles uv tool, pipx, venv, system installs)
PYTHON=""
GRAPHIFY_BIN=$(which graphify 2>/dev/null)
# 1. uv tool installs — most reliable on modern Mac/Linux
if [ -z "$PYTHON" ] && command -v uv >/dev/null 2>&1; then
    _UV_PY=$(uv tool run graphifyy python -c "import sys; print(sys.executable)" 2>/dev/null)
    if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
fi
# 2. Read shebang from graphify binary (pipx and direct pip installs)
if [ -z "$PYTHON" ] && [ -n "$GRAPHIFY_BIN" ]; then
    _SHEBANG=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
    case "$_SHEBANG" in
        *[!a-zA-Z0-9/_.-]*) ;;
        *) "$_SHEBANG" -c "import graphify" 2>/dev/null && PYTHON="$_SHEBANG" ;;
    esac
fi
# 3. Fall back to python3
if [ -z "$PYTHON" ]; then PYTHON="python3"; fi
if ! "$PYTHON" -c "import graphify" 2>/dev/null; then
    if command -v uv >/dev/null 2>&1; then
        uv tool install --upgrade graphifyy -q 2>&1 | tail -3
        _UV_PY=$(uv tool run graphifyy python -c "import sys; print(sys.executable)" 2>/dev/null)
        if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
    else
        "$PYTHON" -m pip install graphifyy -q 2>/dev/null \
          || "$PYTHON" -m pip install graphifyy -q --break-system-packages 2>&1 | tail -3
    fi
fi
# Write interpreter path for all subsequent steps (persists across invocations)
mkdir -p graphify-out
"$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w', encoding='utf-8').write(sys.executable)"
# Save scan root so `graphify update` (no args) knows where to look next time
echo "$(cd INPUT_PATH && pwd)" > graphify-out/.graphify_root

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

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, ensure_ascii=False))
" > 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
  video:    N files (.mp4 .mp3 ...)

Omit any category with 0 files from the summary.

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 > 500: show the warning. Then compute the top 5 first-level subdirectories by file count:
- Read scan_root from the detect JSON (always an absolute path to the resolved INPUT_PATH).
- Concatenate all file lists across all types (code, document, paper, image, video).
- Filter out any path that starts with scan_root + "/graphify-out/" to exclude converted sidecars.
- For each file, strip the scan_root prefix and take the first path component. Files directly in scan_root with no subdirectory count as (root).
- If all files are in (root) with no subdirectories, do not ask to narrow — no subfolders exist. Instead suggest --no-cluster to skip the expensive clustering step and proceed.
- Otherwise rank by count, show the top 5 with file counts, then ask which subfolder to run on. Wait for the user's answer before proceeding.
Otherwise: proceed directly to Step 2.5 if video files were detected, or Step 3 if not.

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

Skip this step entirely if detect returned zero video files.

Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.

Strategy: Read the god nodes from graphify-out/.graphify_detect.json (or the analysis file if it exists from a previous run). You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.

However, if the corpus has only video files and no other docs/code, use the generic fallback prompt: "Use proper punctuation and paragraph breaks."

Step 1 - Write the Whisper prompt yourself.

Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:

Labels: transformer, attention, encoder, decoder → "Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."
Labels: kubernetes, deployment, pod, helm → "DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."

Set it as WHISPER_PROMPT to use in the next command.

Step 2 - Transcribe:

GRAPHIFY_WHISPER_MODEL=base  # or whatever --whisper-model the user passed
$(cat graphify-out/.graphify_python) -c "
import json, os
from pathlib import Path
from graphify.transcribe import transcribe_all

detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')

transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json

After transcription:

Read the transcript paths from graphify-out/.graphify_transcripts.json
Add them to the docs list before dispatching semantic subagents in Step 3B
Print how many transcripts were created: Transcribed N video file(s) -> treating as docs
If transcription fails for a file, print a warning and continue with the rest

Whisper model: Default is base. If the user passed --whisper-model <name>, set GRAPHIFY_WHISPER_MODEL=<name> in the environment before running the command above.

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 (LLM, costs tokens).

Before dispatching subagents: check whether GEMINI_API_KEY or GOOGLE_API_KEY is set. If neither is set, print this one-liner to the user:

Tip: set GEMINI_API_KEY or GOOGLE_API_KEY to use Gemini for semantic extraction (pip install 'graphifyy[gemini]').

Print it once, then continue. If GEMINI_API_KEY or GOOGLE_API_KEY IS set, use graphify.llm.extract_corpus_parallel(files, backend="gemini") for semantic extraction instead of dispatching Claude subagents. The default Gemini model is gemini-3-flash-preview; set GRAPHIFY_GEMINI_MODEL or pass --model in headless CLI flows to override it.

No other API keys are read. If GEMINI_API_KEY/GOOGLE_API_KEY are unset, fall straight through to Claude Code subagent dispatch (Part B below) — the host session itself is the LLM. graphify does not read ANTHROPIC_API_KEY, OPENAI_API_KEY, or any other provider key from the environment. If a host agent prompts the user for ANTHROPIC_API_KEY to run extraction, that prompt is a misread of this skill — ignore it and dispatch subagents as written.

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:

$(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(encoding=\"utf-8\"))
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, cache_root=Path('.'))
    Path('graphify-out/.graphify_ast.json').write_text(json.dumps(result, indent=2, ensure_ascii=False), encoding=\"utf-8\")
    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}, ensure_ascii=False), encoding=\"utf-8\")
    print('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.

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(encoding=\"utf-8\"))
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}, ensure_ascii=False), encoding=\"utf-8\")
Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
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 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.

IMPORTANT - subagent type: Always use subagent_type="general-purpose". Do NOT use Explore - it is read-only and cannot write chunk files to disk, which silently drops extraction results. General-purpose has Write and Bash access which the subagent needs.

Concrete example for 3 chunks:

[Agent tool call 1: files 1-15, subagent_type="general-purpose"]
[Agent tool call 2: files 16-30, subagent_type="general-purpose"]
[Agent tool call 3: files 31-45, subagent_type="general-purpose"]

All three in one message. Not three separate messages.

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

CHUNK_PATH must be an absolute path — derive it before dispatching:

PROJECT_ROOT=$(cat graphify-out/.graphify_root)
# Then for chunk N: CHUNK_PATH="${PROJECT_ROOT}/graphify-out/.graphify_chunk_0N.json"

Subagent prompt template:

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 as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). `file_type` MUST be one of exactly these six values: `code`, `document`, `paper`, `image`, `rationale`, `concept`. Any other value is invalid and will be rejected.
Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. `calls` edges MUST stay within one language: a Python function cannot `calls` a JS/TS/Go/Rust/Java symbol and vice versa — cross-language call edges are phantom artifacts, never emit them.
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: pick exactly ONE value from this set — never 0.5:
    0.95  direct structural evidence (shared data structure, named cross-file reference).
    0.85  strong inference (clear functional alignment, no direct symbol link).
    0.75  reasonable inference (shared problem domain + similar shape, requires interpretation).
    0.65  weak inference (thematically related, no shape evidence).
    0.55  speculative but plausible (surface-level co-occurrence only).
  Models follow discrete rubrics better than continuous ranges; the bimodal
  distribution observed in production (>50% at 0.5, >40% at 0.85+) shows the
  range guidance is being collapsed to a binary. If no value above fits, mark
  the edge AMBIGUOUS rather than picking 0.4 or below.
- AMBIGUOUS edges: 0.1-0.3

Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.

Generate the extraction JSON matching this schema exactly:
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","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}

Then write the JSON to disk using the Write tool at this exact absolute path (no relative paths — Write resolves relative paths against an undefined cwd and the file will be silently lost):
CHUNK_PATH

Step B3 - Collect, cache, and merge

Wait for all subagents. For each result:

Check that graphify-out/.graphify_chunk_NN.json exists on disk — this is the success signal
If the file exists and contains valid JSON with nodes and edges, include it and save to cache
If the file is missing, the subagent was likely dispatched as read-only (Explore type) — print a warning: "chunk N missing from disk — subagent may have been read-only. Re-run with general-purpose agent." Do not silently skip.
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 or are missing, stop and tell the user to re-run and ensure subagent_type="general-purpose" is used.

Merge all chunk files into .graphify_semantic_new.json. After each Agent call completes, read the real token counts from the Agent tool result's usage field and write them back into the chunk JSON before merging — the chunk JSON itself always has placeholder zeros. Then run:

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

chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json'))
all_nodes, all_edges, all_hyperedges = [], [], []
total_in, total_out = 0, 0
for c in chunks:
    d = json.loads(Path(c).read_text(encoding=\"utf-8\"))
    all_nodes += d.get('nodes', [])
    all_edges += d.get('edges', [])
    all_hyperedges += d.get('hyperedges', [])
    total_in += d.get('input_tokens', 0)
    total_out += d.get('output_tokens', 0)
Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({
    'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges,
    'input_tokens': total_in, 'output_tokens': total_out,
}, indent=2, ensure_ascii=False), encoding=\"utf-8\")
print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens')
"

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(encoding=\"utf-8\")) if Path('graphify-out/.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
saved = save_semantic_cache(new.get('nodes', []), new.get('edges', []), new.get('hyperedges', []))
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(encoding=\"utf-8\")) if Path('graphify-out/.graphify_cached.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
new = json.loads(Path('graphify-out/.graphify_semantic_new.json').read_text(encoding=\"utf-8\")) 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, ensure_ascii=False), encoding=\"utf-8\")
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

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(encoding=\"utf-8\"))
sem = json.loads(Path('graphify-out/.graphify_semantic.json').read_text(encoding=\"utf-8\"))

# 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, ensure_ascii=False), encoding=\"utf-8\")
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. If so, pass directed=True to build_from_json() in the code block below. This builds a DiGraph that preserves edge direction (source→target) instead of the default undirected Graph.

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(encoding=\"utf-8\"))
detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))

G = build_from_json(extraction)
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)
labels = {cid: 'Community ' + str(cid) for cid in 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, '.', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
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, ensure_ascii=False), encoding=\"utf-8\")
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')
"

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-out/.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:

$(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(encoding=\"utf-8\"))
detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
analysis   = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))

G = build_from_json(extraction)
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, '.', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
Path('graphify-out/.graphify_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()}, ensure_ascii=False), encoding=\"utf-8\")
print('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.

If --obsidian was given:

If --obsidian-dir <path> was also given, pass it via --dir. Otherwise defaults to graphify-out/obsidian.

graphify export obsidian
# or with custom dir: graphify export obsidian --dir ~/vaults/my-project

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

graphify export html  # auto-aggregates to community view if graph > 5000 nodes
# or: graphify export html --no-viz

Step 6b - Wiki (only if --wiki flag)

Only run this step if --wiki was explicitly given in the original command.

Run this before Step 9 (cleanup) so .graphify_labels.json is still available.

graphify export wiki

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

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

graphify export neo4j

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

graphify export neo4j --push bolt://localhost:7687 --user neo4j --password PASSWORD

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)

graphify export svg

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

graphify export graphml

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

python3 -m graphify.serve graphify-out/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": "python3",
      "args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
    }
  }
}

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

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

graphify benchmark

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

$(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path
from datetime import datetime, timezone
from graphify.detect import save_manifest

# Save manifest for --update
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
# In --update mode, 'all_files' carries the full corpus; 'files' is the changed
# subset. Full-rebuild mode populates only 'files', so the fallback handles that.
save_manifest(detect.get('all_files') or detect['files'])

# Update cumulative cost tracker
extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
input_tok = extract.get('input_tokens', 0)
output_tok = extract.get('output_tokens', 0)

cost_path = Path('graphify-out/cost.json')
if cost_path.exists():
    cost = json.loads(cost_path.read_text(encoding=\"utf-8\"))
else:
    cost = {'runs': [], 'total_input_tokens': 0, 'total_output_tokens': 0}

cost['runs'].append({
    'date': datetime.now(timezone.utc).isoformat(),
    'input_tokens': input_tok,
    'output_tokens': output_tok,
    'files': detect.get('total_files', 0),
})
cost['total_input_tokens'] += input_tok
cost['total_output_tokens'] += output_tok
cost_path.write_text(json.dumps(cost, indent=2, ensure_ascii=False), encoding=\"utf-8\")

print(f'This run: {input_tok:,} input tokens, {output_tok:,} output tokens')
print(f'All time: {cost[\"total_input_tokens\"]:,} input, {cost[\"total_output_tokens\"]:,} output ({len(cost[\"runs\"])} runs)')
"
rm -f graphify-out/.graphify_detect.json graphify-out/.graphify_extract.json graphify-out/.graphify_ast.json graphify-out/.graphify_semantic.json graphify-out/.graphify_analysis.json graphify-out/.graphify_chunk_*.json
rm -f graphify-out/.needs_update 2>/dev/null || true

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

Graph complete. Outputs in PATH_TO_DIR/graphify-out/

  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)

If graphify saved you time, consider supporting it: https://github.com/sponsors/safishamsi

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.

Interpreter guard for subcommands

Before running any subcommand below (--update, --cluster-only, query, path, explain, add), check that .graphify_python exists. If it's missing (e.g. user deleted graphify-out/), re-resolve the interpreter first:

if [ ! -f graphify-out/.graphify_python ]; then
    GRAPHIFY_BIN=$(which graphify 2>/dev/null)
    if [ -n "$GRAPHIFY_BIN" ]; then
        PYTHON=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
        case "$PYTHON" in *[!a-zA-Z0-9/_.-]*) PYTHON="python3" ;; esac
    else
        PYTHON="python3"
    fi
    mkdir -p graphify-out
    "$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w', encoding='utf-8').write(sys.executable)"
fi

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.

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

result = detect_incremental(Path('INPUT_PATH'))
new_total = result.get('new_total', 0)
print(json.dumps(result, indent=2, ensure_ascii=False))
Path('graphify-out/.graphify_incremental.json').write_text(json.dumps(result, ensure_ascii=False), encoding=\"utf-8\")
deleted = list(result.get('deleted_files', []))
if new_total == 0 and not deleted:
    print('No files changed since last run. Nothing to update.')
    raise SystemExit(0)
if deleted:
    print(f'{len(deleted)} deleted file(s) to prune.')
if new_total > 0:
    print(f'{new_total} new/changed file(s) to re-extract.')
"

Then populate .graphify_detect.json so Steps 3A–6 (which read it unconditionally) see the right state for an incremental run. files carries the changed subset (drives Step 3A AST + Step 3B0 cache check on only what changed); all_files carries the full corpus for any step that needs corpus-wide context:

$(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path
r = json.loads(Path('graphify-out/.graphify_incremental.json').read_text(encoding=\"utf-8\"))
Path('graphify-out/.graphify_detect.json').write_text(json.dumps({
    'files': r.get('new_files', {}),
    'all_files': r.get('files', {}),
    'total_files': r.get('new_total', 0),
    'total_words': r.get('total_words', 0),
    'skipped_sensitive': r.get('skipped_sensitive', []),
    'needs_graph': True,
}, ensure_ascii=False), encoding=\"utf-8\")
"

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

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

result = json.loads(open('graphify-out/.graphify_incremental.json', encoding='utf-8').read()) if Path('graphify-out/.graphify_incremental.json').exists() else {}
code_exts = {'.py','.ts','.js','.go','.rs','.java','.cpp','.c','.rb','.swift','.kt','.cs','.scala','.php','.cc','.cxx','.hpp','.h','.kts','.lua','.toc','.f','.F','.f90','.F90','.f95','.F95','.f03','.F03','.f08','.F08'}
new_files = result.get('new_files', {})
all_changed = [f for files in new_files.values() for f in files]
code_only = all(Path(f).suffix.lower() in code_exts for f in all_changed)
print('code_only:', code_only)
"

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): run the full Steps 3A–3C pipeline as normal.

If no new files exist (only deletions), create an empty extraction so the merge step can prune:

if [ ! -f graphify-out/.graphify_extract.json ]; then
    echo '[graphify update] Only deletions -- creating empty extraction for merge.'
    $(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path
Path('graphify-out/.graphify_extract.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
"
fi

Then:

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

# Load new extraction and incremental state
new_extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
incremental = json.loads(Path('graphify-out/.graphify_incremental.json').read_text(encoding=\"utf-8\"))
deleted = list(incremental.get('deleted_files', []))

# Use build_merge() — reads graph.json directly without NetworkX round-trip
# so edge direction (calls, implements, imports) is always preserved (#801).
G = build_merge(
    [new_extraction],
    graph_path='graphify-out/graph.json',
    prune_sources=deleted or None,
)
print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')

# Write merged result back to .graphify_extract.json so Step 4 sees the full graph
merged_out = {
    'nodes': [{'id': n, **d} for n, d in G.nodes(data=True)],
    'edges': [
        # Explicit source/target last so they win over any stale attrs in d.
        {**{k: val for k, val in d.items() if k not in ('_src', '_tgt', 'source', 'target')},
         'source': d.get('_src', u), 'target': d.get('_tgt', v)}
        for u, v, d in G.edges(data=True)
    ],
    # G.graph["hyperedges"] holds hyperedges from both existing graph.json
    # and new_extraction (build_merge combines them). Falling back to
    # new_extraction only would silently drop prior-run hyperedges (#801).
    'hyperedges': list(G.graph.get('hyperedges', [])),
    'input_tokens': new_extraction.get('input_tokens', 0),
    'output_tokens': new_extraction.get('output_tokens', 0),
}
Path('graphify-out/.graphify_extract.json').write_text(json.dumps(merged_out, ensure_ascii=False), encoding=\"utf-8\")
print(f'[graphify update] Merged extraction written ({len(merged_out[\"nodes\"])} nodes, {len(merged_out[\"edges\"])} edges)')

# Save manifest so next --update diffs against today's state, not the
# prior run's baseline (prevents ghost-node reports on subsequent updates).
save_manifest(incremental['files'])
print('[graphify update] Manifest saved.')
"

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

After Step 4, show the graph diff:

$(cat graphify-out/.graphify_python) -c "
import json
from graphify.analyze import graph_diff
from graphify.build import build_from_json
from networkx.readwrite import json_graph
import networkx as nx
from pathlib import Path

# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
G_new = build_from_json(new_extract)

if old_data:
    G_old = json_graph.node_link_graph(old_data, edges='links')
    diff = graph_diff(G_old, G_new)
    print(diff['summary'])
    if diff['new_nodes']:
        print('New nodes:', ', '.join(n['label'] for n in diff['new_nodes'][:5]))
    if diff['new_edges']:
        print('New edges:', len(diff['new_edges']))
"

Before the merge step, save the old graph: cp graphify-out/graph.json graphify-out/.graphify_old.json Clean up after: rm -f graphify-out/.graphify_old.json

For --cluster-only

Skip Steps 1–3. Re-run clustering on the existing graph:

graphify cluster-only .

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

Step 0 — Constrained query expansion (REQUIRED before traversal)

graphify's query CLI matches nodes via case-folded substring + IDF — there is no stemming, no synonyms, no cross-language match inside the binary. If the user's question uses different language or different domain vocabulary than the graph's labels (user says "обработчик" / graph says "handler"; user says "authentication" / graph says "Guardian"), the literal matcher returns 0 hits and the answer collapses to noise.

Fix this without inventing tokens by expanding the query against the actual graph vocabulary first:

Extract the token vocabulary from node labels:

$(cat graphify-out/.graphify_python) -c "
import json, re
from pathlib import Path
data = json.loads(Path('graphify-out/graph.json').read_text())
vocab = set()
for n in data['nodes']:
    for c in re.findall(r'[^\W\d_]+', n.get('label','') or '', re.UNICODE):
        parts = re.findall(r'[A-Z]+(?=[A-Z][a-z])|[A-Z]?[a-z]+|[A-Z]+', c) or [c]
        for p in parts:
            t = p.lower()
            if 3 <= len(t) <= 30:
                vocab.add(t)
Path('graphify-out/.vocab.txt').write_text('\n'.join(sorted(vocab)))
print(f'vocab: {len(vocab)} tokens')
"

Read graphify-out/.vocab.txt. Then for the user's question, select up to 12 tokens from this exact list that semantically match the query intent. Hard constraints:
- You MUST pick only tokens present in the vocabulary file. Do NOT invent tokens.
- If a query concept has no plausible token in the vocab, skip it — do not substitute a near-synonym from training memory.
- If no vocab tokens match the query at all, output an empty list and tell the user the corpus has no relevant vocabulary for this question. Do not fabricate a search.
- Translate cross-language: Russian "аутентификация" → look for auth, credential, token, security IFF present in vocab.
- Morphology: "handlers" maps to handler IFF present; "todos" maps to todo IFF present.
Print the selection explicitly to the user before running the query, so the expansion is auditable:

Query expanded to (from graph vocab, N tokens): [token1, token2, ...]

If the list is empty, say so plainly and stop — do not proceed to traversal.

Step 1 — Traversal

Build the expanded query string by joining the selected tokens with spaces. Use this string as QUESTION below — NOT the original user question. (The original question is preserved only for save-result at the end.)

graphify query "QUESTION"
# or: graphify query "QUESTION" --dfs --budget 3000

Answer using only what the graph output contains. Quote source_location when citing a specific fact. If the graph lacks enough information, say so - do not hallucinate edges.

After writing the answer, save it back into the graph so it improves future queries. Include the expanded tokens inside the --answer text (e.g. "Expanded from original query via vocab: [tokens]. Then traversed...") so the next --update extracts the expansion history as a graph node:

$(cat graphify-out/.graphify_python) -m graphify save-result --question "ORIGINAL_QUESTION" --answer "ANSWER" --type query --nodes NODE1 NODE2

Replace ORIGINAL_QUESTION with the user's verbatim question, ANSWER with your full answer text (containing the expanded-token trace), NODE1 NODE2 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.

graphify path "NODE_A" "NODE_B"

Replace NODE_A and NODE_B with the actual concept names. Then explain the path in plain language - what each hop means, why it's significant.

After writing the explanation, save it back:

$(cat graphify-out/.graphify_python) -m graphify save-result --question "Path from NODE_A to NODE_B" --answer "ANSWER" --type path_query --nodes NODE_A NODE_B

For /graphify explain

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

graphify explain "NODE_NAME"

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:

$(cat graphify-out/.graphify_python) -m graphify save-result --question "Explain NODE_NAME" --answer "ANSWER" --type explain --nodes NODE_NAME

For /graphify add

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

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

try:
    out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR')
    print(f'Saved to {out}')
except ValueError as e:
    print(f'error: {e}', file=sys.stderr)
    sys.exit(1)
except RuntimeError as e:
    print(f'error: {e}', file=sys.stderr)
    sys.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):

YouTube / any video URL → audio downloaded via yt-dlp, transcribed to .txt on next run (requires pip install 'graphifyy[video]')
Twitter/X → fetched via oEmbed, saved as .md with tweet text and author
arXiv → abstract + metadata saved as .md
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.

python3 -m graphify.watch INPUT_PATH --debounce 3

Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:

Code files only (.py, .ts, .go, etc.): re-runs AST extraction + rebuild + cluster immediately, no LLM needed. graph.json and GRAPH_REPORT.md are updated automatically.
Docs, papers, or images: writes a graphify-out/needs_update flag and prints a notification to run /graphify --update (LLM semantic re-extraction required).

Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file.

Press Ctrl+C to stop.

For agentic workflows: run --watch in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual /graphify --update after those waves.

For git commit hook

Install a post-commit hook that auto-rebuilds the graph after every commit. No background process needed - triggers once per commit, works with any editor.

graphify hook install    # install
graphify hook uninstall  # remove
graphify hook status     # check

After every git commit, the hook detects which code files changed (via git diff HEAD~1), re-runs AST extraction on those files, and rebuilds graph.json and GRAPH_REPORT.md. Doc/image changes are ignored by the hook - run /graphify --update manually for those.

If a post-commit hook already exists, graphify appends to it rather than replacing it.

For native CLAUDE.md integration

Run once per project to make graphify always-on in Claude Code sessions:

graphify claude install

This writes a ## graphify section to the local CLAUDE.md that instructs Claude to check the graph before answering codebase questions and rebuild it after code changes. No manual /graphify needed in future sessions.

graphify claude uninstall  # remove the section

Honesty Rules

Never invent an edge. If unsure, use AMBIGUOUS.
Never skip the corpus check warning.
Always show token cost in the report.
Never hide cohesion scores behind symbols - show the raw number.
Never run HTML viz on a graph with more than 5,000 nodes without warning the user.