lingot

The context compiler for AI coding agents. Intelligence blocks that make Cursor, Windsurf, and Claude Code measurably better.

Baseline pass rate without context: 52%. With a single Lingot block: 99.7%.

That's from 6,400+ controlled assertions across 4 experiments. Same model, same prompts. The only variable was context. Read the research →

Quick Start

# Install a block
npx lingot add supabase-auth

# Detect your stack and see recommendations
npx lingot init

# Install multiple blocks
npx lingot add drizzle-orm stripe-billing tailwind-v4

# Score your project's context health
npx lingot doctor

# Compile blocks for your agent
npx lingot compile --target cursor    # .cursorrules
npx lingot compile --target windsurf  # .windsurfrules
npx lingot compile --target claude    # CLAUDE.md

What Are Intelligence Blocks?

Each block contains 4 files of curated, token-optimized context:

| File | Purpose | |------|---------| | knowledge.md | Dense domain knowledge, mental models, current API patterns | | rules.xml | Affirmative heuristic rules that prevent hallucinations | | examples.yaml | Few-shot input/output examples for common tasks | | lingot.json | Metadata, version, dependencies, scope coverage |

Load blocks into your AI agent's context to get measurably better output. Our Drizzle ORM block improved code accuracy from 52% to 99.7% across 5 eval scenarios.

Commands

| Command | Description | |---------|-------------| | lingot add <name> | Install a block from the registry | | lingot remove <name> | Remove installed blocks | | lingot update [name...] | Check for and install block updates | | lingot init | Scan package.json and suggest relevant blocks | | lingot list | List installed blocks with token counts | | lingot inspect <name> | Show block details, version, and scope | | lingot search <query> | Search the registry | | lingot doctor | Score context health 0-100, detect Pink Elephant Tax | | lingot compile | Compile for Cursor, Claude Code, Windsurf | | lingot stats | Show installed blocks summary and token usage | | lingot serve | Start local MCP server for installed blocks |

lingot doctor

Scores your project's context files on three clinical metrics:

• LINT-001: Pink Elephant Tax — Negative rules (NEVER, AVOID, DON'T) that inject deprecated tokens into the model's working memory. Our data shows each violation costs ~2.7% accuracy.
• LINT-002: Attention Dilution — Context blocks that exceed the signal-to-noise sweet spot (>8,000 tokens).
• LINT-003: Latent Collision — Blocks that will interfere when co-loaded due to overlapping scope domains.

npx lingot doctor                    # Score installed blocks
npx lingot doctor ./my-rules-dir     # Score a specific directory
npx lingot doctor --report           # Machine-readable JSON for CI

lingot compile

Compiles installed blocks into the format your agent expects:

# Generate .cursorrules files (one per block)
npx lingot compile --target cursor

# Generate a single .windsurfrules file
npx lingot compile --target windsurf

# Generate a single CLAUDE.md with all blocks
npx lingot compile --target claude

The compiler uses Semantic Lens ordering — knowledge first (the map), then rules (the guardrails), then examples (the demonstrations). This matches our experimental findings: knowledge carries 95%+ of the signal.

77 Blocks Available

Auth, frontend, backend, database, AI SDKs, payments, testing, DevOps, and more.

Browse all blocks at lingot.sh.

Popular blocks

npx lingot add drizzle-orm        # ORM with correct v3 patterns
npx lingot add supabase-auth      # Auth + RLS (v2.0, 4,440 tokens)
npx lingot add tailwind-v4        # CSS-first config, no tailwind.config.js
npx lingot add next-auth          # NextAuth.js v5 with App Router
npx lingot add stripe-billing     # Webhooks, subscriptions, checkout
npx lingot add prisma             # Schema design + Client extensions
npx lingot add zod                # Schema validation + type inference

MCP Integration

Expose installed blocks to any MCP-compatible agent:

lingot serve

Provides two tools:

• search_packages — Find relevant blocks by topic
• get_package_context — Load block content into agent context

The Research

We ran clinical trials to measure how context affects AI code generation:

| Context Level | Pass Rate | vs Baseline | |---|---|---| | No context | 52.0% | — | | Rules only | 69.9% | +17.9pp | | Knowledge only | 99.7% | +47.7pp | | Full block | 97.0% | +45.0pp |

Key findings:

• Structured knowledge beats raw docs — A 4,000-token block outperforms 40,000 tokens of copy-pasted documentation
• Negative rules backfire — "NEVER use X" makes X more salient to the model (the Pink Elephant Tax)
• Context has carrying capacity — Some block combinations create asymmetric interference (Latent Hijacking)

Full methodology, data, and analysis: lingot.sh/research

CI/CD Integration

Gate your PRs on context health:

# .github/workflows/context-lint.yml
name: Context Lint
on: [pull_request]
jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npx lingot doctor --min-score 80 --report

Contributing

Want to create a block for your favorite library? See the contribution guide.

npx lingot create my-block
# Edit the generated files, then validate:
npx lingot validate ./my-block
npx lingot doctor ./my-block

License

MIT