Graphify: A Claude Code Skill That Built a Knowledge Graph of Your Repo — 450k Downloads, 40k Stars in 26 Days

On April 5th, a developer shipped graphify — a Claude Code skill that indexes your entire codebase into a persistent knowledge graph. Type /graphify . and it reads every file, detects communities via the Leiden algorithm, and gives Claude long-term memory of the repo. The key metric: 71x fewer tokens per query vs. reading raw files.
Explosive Adoption
- 26 days after launch: 450k+ PyPI downloads, ~40k GitHub stars
- Peaked at GitHub global rank #2 in its first week
- Spawning Medium articles, YouTube tutorials, and third-party build-ons
Unexpected Use Cases
The creator reports people aren't just using it for code. They're feeding graphify:
- SQL schemas
- Obsidian vaults
- Research paper corpora
- Transcribed meeting recordings
- Whiteboard photos
And querying across all of them with /graphify query "..." — which became the primary command.
What's Missing?
The author is now asking the community: what's broken or missing in your workflow? The repo is actively evolving based on feedback.
Who It's For
Developers using Claude Code who want persistent, low-token-cost context for large codebases or mixed datasets.
📖 Read the full source: r/ClaudeAI
👀 See Also

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Claudeck: Browser UI for Claude Code with Agents, Cost Tracking, and Plugin System
Claudeck is a browser-based UI that wraps the Claude Code SDK, featuring autonomous agent orchestration, cost tracking, git worktree isolation, persistent memory, and a plugin system. Install with npx claudeck@latest.

AutoSkillUpdate: A Claude Code Plugin That Detects Outdated Skills
AutoSkillUpdate is an open-source Claude Code plugin that scans your codebase, compares it against existing skills, and identifies drift. It provides drift reports with file paths and line references, then offers to rewrite outdated skills with user confirmation.

Claude 4.6 Opus Reasoning Distilled to 14GB for Apple Silicon via MLX Quantization
A developer has quantized a Qwen 3.5 27B model distilled from Claude 4.6 Opus reasoning trajectories from 55.6GB to 14GB using MLX for Apple Silicon, achieving ~16 tokens/sec on an M4 Pro while maintaining the model's analytical reasoning capabilities.