> ## Documentation Index
> Fetch the complete documentation index at: https://docs.twill.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Legibility scorecard

> Score how ready your repository is for coding agents — free, at twill.ai/score.

The scorecard grades a GitHub repository on **agent legibility**: how easily a coding agent can bootstrap, navigate, and validate work in it. Run it at [twill.ai/score](https://twill.ai/score) — paste a public repo URL, or install the GitHub App for private repos (analyzed with a short-lived, scoped token).

Analysis is static — no dependencies installed, no code executed. It's based on OpenAI's [agentic legibility](https://openai.com/index/practice-of-agentic-legibility/) work and runs in a sandboxed container.

## What it measures

Seven metrics, each scored 0–3 (max 21), with letter grades A (85%+), B (70%+), C (50%+), D below:

| Metric                     | Question it answers                                           |
| -------------------------- | ------------------------------------------------------------- |
| Bootstrap self-sufficiency | Can an agent set the project up from the repo alone?          |
| Task entrypoints           | Are build/test/dev commands discoverable (Makefile, scripts)? |
| Validation harness         | Can changes be verified mechanically (tests, CI)?             |
| Lint & format gates        | Are style rules enforced, not tribal knowledge?               |
| Agent repo map             | Is there an `AGENTS.md`/`CLAUDE.md` orienting agents?         |
| Structured docs            | Do docs explain architecture and conventions?                 |
| Decision records           | Are past decisions written down (ADRs, design docs)?          |

A higher score means agents — Twill's included — waste less time rediscovering your setup and produce better-verified work. The report includes concrete fixes for each low metric.