graphify

Files (2)

• .graphify_version
• SKILL.md

SKILL.md (first lines)

# /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> --update                             # incremental - re-extract only new/changed files
/graphify <path> --directed                            # build directed graph (preserves edge direction: 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-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/graphify add <url>                                   # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                   # tag who wrote it
/graphify add <url> --contributor "Name"              # tag who added it to the corpus
/graphify query "<question>"                          # BFS traversal - broad context
/graphify query "<question>" --dfs                    # DFS - trace a specific path
/graphify query "<question>" --budget 1500            # cap answer at N tokens
/graphify path "AuthModule" "Database"                # shortest path between two concepts
/graphify explain "SwinTransformer"                   # plain-language explanation of a node
```

## What graphify is for

graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.

Three things it does that Claude alone cannot:
1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything.
2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.

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

## 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

```bash
# Detect the correct Python interpreter (handles pipx, venv, system installs)
GRAPHIFY_BIN=$(which graphify 2>/dev/null)
if [ -n "$GRAPHIFY_BIN" ]; then
    PYTHON=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
    case "$PYTHON" in
        *[!a-zA-Z0-9/_.-]*) PYTHON="python3" ;;
    esac
else
    PYTHON="python3"
fi
"$PYTHON" -c "import graphify" 2>/dev/null || "$PYTHON" -m pip install graphifyy -q 2>/dev/null || "$PYTHON" -m pip install graphifyy -q --break-system-packages 2>&1 | tail -3
# Write interpreter path for all subsequent steps (persists across invocations)
mkdir -p graphify-out
"$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w').write(sys.executable)"
```

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

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

Replace INPUT_PATH with the actual path the user provided. Do NOT cat or print the JSON - read it silently and present a clean summary instead:

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

Then act on it:
- If `total_files` is 0: stop with "No supported files found in [path]."
- If `skipped_sensitive` is non-empty: mention file count skipped, not the file names.
- If `total_words` > 2,000,000 OR `total_files` > 200: show the warning and the top 5 subdirectories by file count, then ask which subfolder to run on. Wait for the user's answer before proceeding.
- Otherwise: proceed directly to Step 3 - no need to ask anything.

### 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:

From veverkap/skills, MIT.

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