hayagriva-llm

Structured LLM metadata for Node.js packages — the first standard for machine-readable package context in the npm ecosystem. Generates llm.package.json and llm.package.txt for indexing, search, and IDE tooling (e.g. Cursor, Antigravity).

📖 Documentation: Full docs are built with Docusaurus and deployed to GitHub Pages. See Deploying the docs for setup. After deployment, the site is available at:

https://prakhardubey2002.github.io/hayagriva-llm/

Install

npm install -g hayagriva-llm
# or
npx hayagriva-llm generate

Requirements: Node.js 18+

Usage

From your package root:

hayagriva-llm generate [options]

Examples:

# Static mode (no API key): extract exports from TypeScript/JavaScript entry
hayagriva-llm generate

# AI mode: richer metadata (summary, side effects, keywords) via OpenRouter
hayagriva-llm generate --mode ai

# AI with custom model and full source context
hayagriva-llm generate --mode ai --model openai/gpt-4o --include-src --verbose

Environment

Copy .env.example to .env and set OPEN_ROUTER_API_KEY (and optionally OPEN_ROUTER_MODEL) for AI mode. Legacy names OPENROUTER_API_KEY and HAYAGRIVA_LLM_MODEL are still supported.

Output

• llm.package.json — Structured metadata: name, version, description, exports, hooks, frameworks, optional summary, sideEffects, keywords; IDE- and search-friendly.
• llm.package.txt — LLM-optimized plain-text summary for context windows and retrieval.

Flow (high level)

flowchart TB
  subgraph CLI
    A[hayagriva-llm generate] --> B[Load package.json]
    B --> C[Detect entry file]
    C --> D{Mode?}
  end

  subgraph Static["Static mode"]
    D -->|static| E[ts-morph: extract exports, JSDoc, hooks]
    E --> F[Build metadata]
  end

  subgraph AI["AI mode (guardrails)"]
    D -->|ai| G[Step 1: Package overview]
    G --> H[Validate: summary, sideEffects, keywords, frameworks]
    H --> I[Step 2: Exports]
    I --> J[Validate: exports map, hooks]
    J --> K[Merge steps]
    K --> F
  end

  F --> L[Write llm.package.json]
  F --> M[Write llm.package.txt]

Detailed flow (entry detection, validation, and file layout) is in the documentation site (see Flow & architecture in the repo).

Using hayagriva-llm in your package

Add it as a devDependency so your package always ships up-to-date LLM metadata.

1. Install

npm install -D hayagriva-llm

2. Generate metadata (manual or script)

From your package root:

# Static mode — no API key; uses ts-morph on your entry file
npx hayagriva-llm generate

# AI mode — set OPEN_ROUTER_API_KEY in .env first
npx hayagriva-llm generate --mode ai

This writes llm.package.json and llm.package.txt in the current directory. Commit them so consumers and tooling (e.g. Cursor, Antigravity) can use them.

3. Add an npm script (optional)

In your package.json:

{
  "scripts": {
    "llm:generate": "hayagriva-llm generate",
    "prepublishOnly": "npm run llm:generate"
  }
}

• llm:generate — run whenever you want to refresh metadata.
• prepublishOnly — regenerates metadata before npm publish so the published package always has current exports.

For AI mode in scripts, ensure OPEN_ROUTER_API_KEY (and optionally OPEN_ROUTER_MODEL) are set in your environment or in a .env file. The CLI loads .env via dotenv automatically.

Automating with Husky

Use Husky to run hayagriva-llm generate automatically (e.g. before commit) so llm.package.json and llm.package.txt stay in sync without manual runs.

1. Install Husky

npm install -D husky
npx husky init

This creates .husky/ and a default pre-commit hook.

2. Hook: regenerate metadata before commit

Edit .husky/pre-commit so it runs the generator and re-stages the output:

# Regenerate LLM metadata (uses .env for OPEN_ROUTER_API_KEY if you use --mode ai)
npx hayagriva-llm generate

# Re-stage generated files so they are included in the commit
git add llm.package.json llm.package.txt

• Static mode: No env needed; the hook just runs hayagriva-llm generate (default mode is static).

• AI mode: Set OPEN_ROUTER_API_KEY (and optionally OPEN_ROUTER_MODEL) in .env in the repo root. The CLI loads .env automatically. Example hook for AI mode:

  npx hayagriva-llm generate --mode ai
  git add llm.package.json llm.package.txt

3. Combine with lint / test (optional)

Run lint and tests in the same hook, then generate metadata:

# Example: lint and test first, then regenerate metadata
npm run lint
npm test
npx hayagriva-llm generate
git add llm.package.json llm.package.txt

Adjust lint / test to match your package.json scripts.

4. Different hooks to fit your workflow

Example pre-push (.husky/pre-push):

npm test
npx hayagriva-llm generate
git add llm.package.json llm.package.txt

Automating with GitHub Actions

Run hayagriva-llm generate in CI to validate that metadata is present and up to date, or to publish it as an artifact.

Example: check metadata on push/PR

Create .github/workflows/llm-metadata.yml:

name: LLM metadata

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  generate-and-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Node
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Install hayagriva-llm
        run: npm install -D hayagriva-llm

      - name: Generate LLM metadata (static)
        run: npx hayagriva-llm generate

      - name: Check metadata is committed
        run: |
          git diff --exit-code llm.package.json llm.package.txt || \
            (echo "::error::llm.package.json or llm.package.txt are out of date. Run: npx hayagriva-llm generate" && exit 1)

This fails the workflow if someone forgets to run the generator after changing exports.

Example: generate with AI in CI (optional)

If you want AI mode in CI, add your OpenRouter key as a repo secret (e.g. OPEN_ROUTER_API_KEY) and run:

- name: Generate LLM metadata (AI)
  env:
    OPEN_ROUTER_API_KEY: ${{ secrets.OPEN_ROUTER_API_KEY }}
  run: npx hayagriva-llm generate --mode ai

Then use the same “check metadata is committed” step, or upload llm.package.json / llm.package.txt as artifacts.

Docs

The full documentation is built with Docusaurus. Source lives in website/docs/; the build output is written to the repo docs/ folder so GitHub Pages can deploy from it (GitHub allows only the docs folder or the root as the deployment source).

Run the docs locally: npm run docs:install then npm run docs:start (from repo root).

Deploying the docs (GitHub Pages from /docs)

1. Build to docs/: Run npm run docs:build from repo root. Docusaurus writes the static site into docs/.
2. Enable GitHub Pages: In the repo Settings → Pages → Build and deployment → Source, choose Deploy from a branch. Select branch main and folder /docs. Save.
3. Commit and push: Commit the built docs/ folder and push to main. The workflow Build docs (for GitHub Pages /docs) can build Docusaurus to docs/ and commit it on push to main, or you can run npm run docs:build locally and commit docs/ yourself.

The site will be at https://prakhardubey2002.github.io/hayagriva-llm/ (see website/docusaurus.config.js).

Pre-commit (Husky)

This repo uses Husky for pre-commit hooks. On commit, the hook runs:

1. Lint — npm run lint (ESLint on src/ and test/)
2. Test — npm test (Vitest)
3. Build — npm run build
4. Size limit — npx size-limit (checks dist/cli.cjs and dist/cli.mjs stay under 50 kB)

Install once: npm install. The prepare script runs husky so the .husky/pre-commit hook is installed.

Publishing to npm

1. Login — npm login (create an account at npmjs.com if needed).
2. Publish — From the package root run:

   npm publish

   prepublishOnly will run lint, tests, and build before publishing. Only the dist/ folder is included (files in package.json); README and LICENSE are included by npm by default.

Repository: github.com/prakhardubey2002/hayagriva-llm · npm: hayagriva-llm. For a scoped package (e.g. @your-org/hayagriva-llm), set "name": "@your-org/hayagriva-llm" and run npm publish --access public.

License

MIT