Show HN: I mapped my AI coding setup – 90 of 103 installed skills never fire

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A map of your AI setup. Agent Atlas scans your AI coding environment and turns it into an interactive mind map — so you can see, at a glance, what your agents and skills are actually good at, what you never use, and what's missing.

You've probably installed dozens of skills, subagents, and MCP servers into your AI tools. But do you actually know what your setup can do? Agent Atlas reads your configuration and your session history, then draws your whole stack as a living map: every skill, agent, and tool as a node, grouped by what it's for, sized by how often you actually use it.

In one picture you can answer questions you currently can't:

What is my setup tuned for? More engineering than writing? Any research capability at all?

What am I paying for but never using? Every installed MCP server loads its tool schemas into context in every session — unused servers silently cost you tokens every single day.

Where are the overlaps and gaps? Two skills doing nearly the same job; whole capability areas with zero coverage.

Quick start

npx agent-atlas-cli

That's it. No config, no account. Works on Claude Code setups today (Cursor and friends are on the roadmap — the scanner is built behind an adapter interface).

Don't have an Anthropic API key handy? It still works:

npx agent-atlas-cli --rough # keyword-based classification, no API call at all

Privacy — read this part

Agent Atlas is read-only and local-only:

It never modifies anything on your machine.

Nothing leaves your machine except one optional classification API call — and that call sends only the names and descriptions of your skills/agents/servers. Your session transcripts, your code, and your prompts are never sent anywhere.

With --rough, nothing leaves your machine at all.

It's open source (MIT). Don't take our word for any of this — read the code.

How it works

Four stages, one pipeline:

Scanner → Usage Miner → Classifier → Renderer (what's (what actually (what each (the map + installed) fires) piece is for) diagnostics)

Scanner — inventories skills, subagents, MCP servers, and hooks from your ~/.claude configuration (plus the current project's .claude/).

Usage Miner — streams your local session transcripts and counts what actually fired in the last 30 days (--days to change the window).

Classifier — one cheap LLM pass scores every item across five capability axes: engineering, writing, research, design, ops. Results are cached by content hash, so re-runs are fast and nearly free. Got a classification wrong? Pin the right one in ~/.agent-atlas/overrides.json — overrides always win.

Renderer — draws the interactive map: clusters by capability, node size = usage, grey = dead weight, plus a "tuning bar" summarizing your whole stack (Engineering 61% · Research 17% · …). Below the map, three diagnostic lists: dead weight (with estimated tokens wasted per session), overlaps (near-duplicate skills/agents), and gaps (capability axes you barely cover). The "Share card" button exports a PNG:

Usage

npx agent-atlas-cli # scan, classify, open the map npx agent-atlas-cli --json # dump inventory + usage + classification as JSON npx agent-atlas-cli --days 90 # widen the usage window npx agent-atlas-cli --rough # skip the API, use keyword heuristics npx agent-atlas-cli --atlas-dir DIR # custom location for cache + overrides

Classification uses your ANTHROPIC_API_KEY environment variable if set; otherwise it falls back to rough mode automatically.

Status / roadmap

Milestone What Status

M1 Scanner + Usage Miner (--json output) ✅ done

M2 Classifier — LLM pass, cache, overrides, no-key fallback ✅ done

M3 Renderer — interactive map + tuning bar (atlas.html) ✅ done

M4 Diagnostics (dead weight, overlaps, gaps) + shareable card ✅ done

v2 Adapters for Cursor, Codex CLI, Gemini CLI; recommendations 💭 planned

The full design lives in SPEC.md.

Development

npm install npm run build # tsc → dist/ npm test # vitest, runs against the fixture tree in fixtures/

All tests run against a fake ~/.claude tree in fixtures/ — nothing in the test suite touches your real setup. If you're adding a scanner or classifier change, extend the fixtures and the expected outputs alongside it.

Releasing

Merging to main runs CI only. Publishing to npm happens when a version tag is pushed:

npm version patch # or minor / major — bumps package.json, commits, tags vX.Y.Z git push origin main --follow-tags

The publish workflow then verifies the tag matches package.json, runs the test suite, builds, and publishes to npm via trusted publishing (OIDC) — no tokens involved.

Contributions welcome — especially adapter implementations for other AI coding tools and hand-labeled classification examples for the rubric.

Teams

Curious what your whole engineering team's AI stack looks like — aggregate maps, redundant spend, shadow tooling? I'm exploring a team version. Email [email protected].

License

MIT © 2026 Alfred Emmanuel

About

Map your AI coding setup — see what your agents and skills are actually good at, what you never use, and what's missing. npx agent-atlas-cli

www.npmjs.com/package/agent-atlas-cli

Topics

cli

mcp

npm-package

data-visualization

developer-tools

d3js

ai-agents

claude

claude-code

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