Stacklit

108,000 lines of code. 4,000 tokens of index.

One command makes any repo AI-agent-ready. No server, no setup.

Install and run

npx stacklit init

That is it. Downloads the binary, scans your codebase, generates the index, opens the visual map. One command.

Other install options:

npm install -g stacklit              # install globally, then run: stacklit init
go install github.com/glincker/stacklit/cmd/stacklit@latest

Or grab a binary from GitHub Releases (macOS, Linux, Windows).

CI / GitHub Action

Use glincker/stacklit-action to keep the index fresh automatically. Auto-commit on push, or gate PRs with check mode:

- uses: actions/checkout@v4
- uses: glincker/stacklit-action@v1        # auto-commit (default)
# or: with: { mode: check }               # fail PR if index is stale

Add permissions: contents: write to the job when using auto-commit mode.

What happens when you run it

$ stacklit init
[stacklit] found 342 files
[stacklit] parsed 342 files (0 errors)
[stacklit] done in 89ms -- wrote stacklit.json, DEPENDENCIES.md, stacklit.html

Opening visual map...

Three files appear in your project:

File	What it is	Commit it?
stacklit.json	Codebase index for AI agents	Yes
DEPENDENCIES.md	Mermaid dependency diagram	Yes (renders on GitHub)
stacklit.html	Interactive visual map (4 views)	No (gitignored, regenerates)

git add stacklit.json DEPENDENCIES.md
git commit -m "add stacklit codebase index"

Done. Every AI agent that opens this repo can now read stacklit.json instead of scanning files.

Why

AI coding agents burn most of their context window figuring out where things live. Reading one large file to find a function signature costs thousands of tokens. Five agents on the same repo each rebuild the same mental model from scratch.

Without stacklit: Agent reads 8-12 files. ~400,000 tokens. 45 seconds before writing a line.

With stacklit: Agent reads stacklit.json. ~4,000 tokens. Knows the structure instantly.

Token efficiency (measured on real projects)

Project	Language	Lines of code	Index tokens
Express.js	JavaScript	21,346	3,765
FastAPI	Python	108,075	4,142
Gin	Go	23,829	3,361
Axum	Rust	43,997	14,371

See examples/ for full outputs.

What is in stacklit.json

{
  "modules": {
    "src/auth": {
      "purpose": "Authentication and session management",
      "files": 8, "lines": 1200,
      "exports": ["AuthProvider", "useSession()", "loginAction()"],
      "depends_on": ["src/db", "src/config"],
      "activity": "high"
    }
  },
  "hints": {
    "add_feature": "Create handler in src/api/, add route in src/index.ts",
    "test_command": "npm test"
  }
}

Modules, dependencies, exports with signatures, type definitions, git activity heatmap, framework detection, and hints for where to add features and how to run tests.

Set up your AI tools

One command (recommended)

stacklit setup

Auto-detects Claude Code, Cursor, and Aider. For each:

• Injects a compact ~250-token codebase map into the tool's config file
• Configures MCP server integration
• Installs a git hook to keep the map fresh on every commit

Or configure a specific tool:

stac

... [View full README on GitHub](https://github.com/glincker/stacklit#readme)