What does this skill do, and when should you use it?
Graphify is a Claude Code skill invoked with /graphify that extracts an entity-relationship knowledge graph from a folder or GitHub repository. It detects files, optionally transcribes audio/video, then combines deterministic AST extraction of code with LLM subagent (or Gemini API) semantic extraction, followed by community detection. It produces three outputs — interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md — and persists the graph in graphify-out/ so later questions query it directly. Every edge carries an honest EXTRACTED/INFERRED/AMBIGUOUS audit label, and multiple repos can be merged into one cross-repo graph.
1) Detects code, docs, PDFs, images, and audio/video in a target path, warning on oversized corpora; 2) transcribes video/audio to text via Whisper with a domain-hinted prompt; 3) runs free deterministic AST extraction on code in parallel with LLM subagents over docs/images (cached, chunked 20-25 files each); 4) merges extraction, builds the graph, runs community detection, finds god nodes and surprising cross-document connections; 5) generates GRAPH_REPORT.md, graph., interactive HTML, plus optional Obsidian vault, SVG, GraphML, Neo4j Cypher export/push, and an agent-crawlable wiki; 6) offers /graphify query with BFS/DFS traversal to answer natural-language questions, /graphify path for shortest paths between concepts, and an MCP stdio server exposing query_graph and related tools to other agents.
- A developer onboarding onto an unfamiliar codebase: clone the repo, run /graphify, then ask 'How does X work?' or 'What calls Y?' without rereading all sources
- A research group: drop a folder of papers and notes in and let community detection surface cross-document concept links
- A multi-service team: extract each subfolder of a monorepo separately, then merge into one cross-service graph with merge-graphs
- A content creator or podcaster: transcribe recorded audio/video and graph it together with written notes into an Obsidian vault
- A Neo4j user: push exported Cypher into an existing Neo4j instance to plug into current graph workflows
How do you install this skill?
- The skill auto-installs the third-party PyPI package 'graphifyy' (including a --break-system-packages fallback) — a supply-chain trust risk; audit the package before first run.
- The description's trigger is overly broad: it takes over any codebase question whenever graphify-out/ exists, risking false invocation; prefer explicit /graphify invocation only.
- Core function depends on GitHub, PyPI and Gemini APIs; mainland-China reachability is poor and Chinese-language support is undeclared.
- SKILL.md is truncated at the node-ID specification; runtime may stall on missing instructions.
- The skill has no version or changelog; update behavior is unpredictable.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
Python 3graphifyy PyPI packageGemini API key (optional, for semantic extraction)Whisper (for audio/video transcription)
The skill lives at .claude/skills/graphify/SKILL.md in the career-pilot repo; place it in your Claude Code skills directory (typically your project's .claude/skills/). On first run it auto-installs the Python package: uv tool install --upgrade graphifyy, or pip install graphifyy without uv. Optional: set GEMINI_API_KEY or GOOGLE_API_KEY for Gemini-based semantic extraction (pip install 'graphifyy[gemini]'). The repo README describes the Career Pilot web application, not this skill; treat SKILL.md as the authoritative install reference.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/anurag3407/career-pilot.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/.claude/skills/graphify" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Trigger /graphify in Claude Code. Common forms: /graphify <path> builds a full graph; /graphify https://github.com/<owner>/<repo> clones then builds; --update for incremental re-extraction; --mode deep for richer INFERRED edges; --neo4j-push bolt://localhost:7687 to push to Neo4j; --mcp to start an MCP server; /graphify query "<question>" to query an existing graph (add --dfs or --budget N as needed); /graphify explain "<node>" for plain-language explanations; /graphify --help prints usage. Once graphify-out/graph. exists, natural-language codebase questions automatically take the fast path and query the graph without rebuilding.
What are this skill's strengths and limitations?
- Extremely broad input: code, docs, PDFs, images, audio, and video all become graph nodes
- AST structural extraction is free and deterministic; semantic results are cached, keeping reruns cheap
- EXTRACTED/INFERRED/AMBIGUOUS audit trail makes graph confidence transparent
- Rich export ecosystem: HTML, GraphRAG JSON, Obsidian, Neo4j, SVG, GraphML, wiki, MCP server
- Supports incremental updates, cross-repo merging, and a --watch mode that rebuilds on code changes
- Core semantic extraction orchestration depends on Claude Code's parallel subagent mechanism; porting elsewhere requires rework
- Without a Gemini key it leans on the host LLM session, so token cost scales with corpus size
- SKILL.md was presented truncated; full content and standalone test coverage cannot be verified
- It sits inside a career-platform (Career Pilot) repository, so its update cadence is tied to an unrelated project
- Corpora over ~2M words or 500 files require narrowing to a subfolder before extraction
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Graphify — Folder-to-Knowledge-Graph this page | 44 · Not recommended | ★ 133 | 22d ago | MIT |
| SwarmVault | 55 · Use with care | ★ 716 | 3mo ago | MIT |
| Skill Seekers Skill Builder | 48 · Use with care | ★ 15k | 25d ago | MIT |
| Open Code Review — AI Code Quality Gate | 46 · Use with care | ★ 40 | 6mo ago | NOASSERTION |
| Vexor Semantic Code Search | 60 · Recommended | ★ 244 | 23d ago | MIT |
The skill positions itself against generic GraphRAG pipelines (its JSON is 'GraphRAG-ready') and integrates with existing graph tooling like Neo4j, Gephi/GraphML, and Obsidian, but the source material names no direct one-shot competitor.
How did FollowSkills review this skill?
Some data-flow disclosure: claims to read only GEMINI/GOOGLE keys, skips sensitive files, local Whisper prompts. But SKILL.md instructs the agent to auto-install the third-party PyPI package 'graphifyy' (with a --break-system-packages fallback), an unverified supply-chain dependency; it auto-clones arbitrary GitHub repos into ~/.graphify; no rollback instructions beyond graphify-out. Deducted for: unconfirmed external installs and arbitrary cloning, more-than-least-privilege behavior, incomplete recovery.
Instructions are structurally coherent with caching/chunking and partial failure handling (zero files, transcription failure, cache hits), but there is zero test evidence; key paths (AST extraction, semantic subagents, Neo4j push) are unverifiable statically; SKILL.md is truncated mid-specification at the node-ID rules, so later steps are missing and execution could stall. Deducted for: no tests, truncated file, thin diagnosable failure feedback.
Scenario is clear (knowledge-graph queries over code/docs) with detailed parameters and outputs; however the description's trigger is over-broad ('any question about a codebase', taking over whenever graphify-out/ exists), risking false invocation; core function depends on GitHub, PyPI, Gemini/Whisper with no declared Chinese support and poor mainland-China reachability. Deducted for: loose trigger boundaries, undeclared non-fit ranges, environment reachability risk.
Repo is MIT-licensed, SKILL.md is layered (usage/purpose/steps), README shows maintainers and contribution paths; but the skill itself has no version, changelog, or known-limitations section, the publisher is registry-unverified, and the truncation suggests incomplete maintenance. Deducted for: missing versioning/governance and undisclosed limits.
Claimed outputs (HTML graph, GraphRAG JSON, GRAPH_REPORT.md) would beat manual graph-building, but static review cannot confirm any output is directly usable; entries like '--html is a no-op' hint at doc/behavior drift. Deducted for: no representative output evidence; comparative benefit rests on claims.
Only one skill instruction file; no tests or CI coverage for the skill (repo CI covers frontend builds and deploy tokens only), no third-party execution evidence. Deducted for: no independently reproducible material behind key claims.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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