Graphify

/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                                       # full pipeline on specific path
/graphify https://github.com//           # clone repo then run full pipeline on it
/graphify https://github.com// --branch   # clone a specific branch
/graphify   ...                           # clone multiple repos, build each, merge into one cross-repo graph
/graphify  --mode deep                          # thorough extraction, richer INFERRED edges
/graphify  --update                             # incremental - re-extract only new/changed files
/graphify  --directed                            # build directed graph (preserves edge direction: source→target)
/graphify  --whisper-model medium                # use a larger Whisper model for better transcription accuracy
/graphify  --cluster-only                       # rerun clustering on existing graph
/graphify  --no-viz                             # skip visualization, just report + JSON
/graphify  --html                               # (HTML is generated by default - this flag is a no-op)
/graphify  --svg                                # also export graph.svg (embeds in Notion, GitHub)
/graphify  --graphml                            # export graph.graphml (Gephi, yEd)
/graphify  --neo4j                              # generate graphify-out/cypher.txt for Neo4j
/graphify  --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify  --falkordb                           # generate graphify-out/cypher.txt for FalkorDB
/graphify  --falkordb-push falkordb://localhost:6379   # push directly to FalkorDB
/graphify  --mcp                                # start MCP stdio server for agent access
/graphify  --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify  --wiki                               # build agent-crawlable wiki (index.md + one article per community)
/graphify  --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/graphify add                                    # fetch URL, save to ./raw, update graph
/graphify add  --author "Name"                   # tag who wrote it
/graphify add  --contributor "Name"              # tag who added it to the corpus
/graphify query ""                          # BFS traversal - broad context
/graphify query "" --dfs                    # DFS - trace a specific path
/graphify query "" --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 "" 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 - GitHub repos and multi-path merge (only if a URL or several paths)

Only when the path is one or more https://github.com/... URLs, or several local subfolders to merge. See references/github-and-merge.md for the clone, cross-repo merge, and monorepo flow, then continue with the resolved local path. A plain local path skips this step.

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 - Video and audio (only if video files detected)

Skip this step entirely if detect returned zero video files. When the corpus has video or audio, see references/transcribe.md to transcribe them to text first, then treat the transcripts as doc files in Step 3.

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('INPUT_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. First write an empty semantic file so Part C's merge has its input (it reads .graphify_semantic.json unconditionally; without this a code-only run hits FileNotFoundError):

$(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path
Path('graphify-out/.graphify_semantic.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
"

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\"))
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]

cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files, root='INPUT_PATH')

# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .graphify_cached.json (#1392).
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\")
else:
    Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
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 messag

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zdnemz
• Source: zdnemz/claude-team
• License: MIT

Install and usage instructions live in the source repository linked above.