AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻译准备中
agentlint 扫描任何代码库,并为 Claude Code、Cursor、Codex、Copilot 和 Gemini CLI 提供 0–100 的就绪评分。本地。免费。30 秒。
AI 翻译
agentlint scans any codebase and gives it a 0–100 readiness score for Claude Code, Cursor, Codex, Copilot, and Gemini CLI. Local. Free. 30 seconds.
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个人 GitHub 账号下公开仓库的所有者可以登录并验证所有权,自行管理介绍,无需管理员批准。
登录并生成验证文件,在 30 分钟内将其提交到此仓库的默认分支。其他分支或分叉仓库中的文件无效。然后在下方检查文件,即可无需管理员批准直接管理项目。请保留文件以维持管理权限。
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在项目文件夹中运行 /nomorevibe verify,即可验证所有权并自行管理介绍。
一分钟完成验证AI 编码代理的灯塔。审计任何代码库,评估其对 Claude Code、Cursor、Codex、Copilot 和 Gemini CLI 的准备程度。30 秒内给出 0–100 分数。
AI 翻译
The Lighthouse for AI coding agents. Audit any repo for how ready it is for Claude Code, Cursor, Codex, Copilot, and Gemini CLI. 0–100 score in 30 seconds.
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
The Lighthouse for AI coding agents. Audit any repo for how ready it is for Claude Code, Cursor, Codex, Copilot, and Gemini CLI. Get a 0–100 score and a fix list in 30 seconds.
That's it. No install, no signup. Drop the command into any repo and you'll get a colored report in your terminal plus a full HTML report on disk.
Or install once and use the short command:
End-users invoke it via npx/pnpm dlx/bunx regardless of what package manager their project uses — the published @agentlinthq/cli package is universal.查看完整 README ↗The Lighthouse for AI coding agents. Audit any repo for how ready it is for Claude Code, Cursor, Codex, Copilot, and Gemini CLI. Get a 0–100 score and a fix list in 30 seconds.
npx @agentlinthq/cli
That's it. No install, no signup. Drop the command into any repo and you'll get a colored report in your terminal plus a full HTML report on disk.
# Scan the current directory
npx @agentlinthq/cli
# Scan a different path
npx @agentlinthq/cli ./packages/api
# Machine-readable output for CI or for AI agents to parse
npx @agentlinthq/cli --json > report.json
# Markdown report for AI agents to consume directly
npx @agentlinthq/cli --markdown
# Also audit the docs site
npx @agentlinthq/cli --url https://docs.example.com
# Print a copy-paste fix prompt for your AI agent (no AI involved — predefined templates)
npx @agentlinthq/cli prompt
Or install once and use the short command:
npm i -g @agentlinthq/cli
agentlint
End-users invoke it via npx/pnpm dlx/bunx regardless of what package manager their project uses — the published @agentlinthq/cli package is universal.
Note on the name. The unscoped
agentlintname on npm is held by an unrelated package, and the matching org name was not available either, so the npm org isagentlinthq. The CLI is published as@agentlinthq/cli; the binary it installs is stillagentlint. See ADR-0011.
Every AI coding tool now reads project context from a config file:
| Tool | File it reads |
|---|---|
| Claude Code | CLAUDE.md (or AGENTS.md) |
| OpenAI Codex CLI | AGENTS.md |
| Cursor | .cursor/rules/ |
| GitHub Copilot | .github/copilot-instructions.md |
| Gemini CLI | GEMINI.md (or AGENTS.md) |
If you don't have any of these, the agent guesses. It guesses badly. The fix is simple: write the right files. agentlint tells you which ones are missing and what's in them.
Five categories, 30 checks, 0–100 score. See AGENTS.md for project conventions.
For agent integration: agentlint --json and agentlint --markdown produce structured reports. Tell Claude Code or Cursor "run agentlint and fix what's failing" and it'll do the work.
agentlint prompt)agentlint doesn't just diagnose — it hands you the remediation. Every failed check carries a predefined fix prompt (a static template parametrized with your detected package manager, language, and workspaces — agentlint never calls an AI). Get them three ways:
# One consolidated prompt covering every finding, ordered by score impact.
# Paste it into Claude Code, Cursor, Copilot, Codex, or any coding agent.
agentlint prompt
# Just one rule
agentlint prompt --rule agents-md-exists
# Straight to the clipboard (macOS)
agentlint prompt | pbcopy
The prompts also appear per-finding in the --markdown and --json outputs
(fix.prompt) and as collapsible blocks in the HTML report.
This repo ships a ready-made skill at skills/agentlint/SKILL.md
that teaches any agent the audit → fix → verify workflow (using the installed
agentlint binary, or npx -y @agentlinthq/cli@latest when not installed).
For Claude Code:
cp -r skills/agentlint ~/.claude/skills/agentlint
Then ask: "audit this repo's agent readiness and fix the findings".
--push)Off by default — the CLI is local-first and never phones home unless you opt in.
If you want a history of runs at https://agentlint.sh/dashboard:
# Generate a token at https://agentlint.sh/dashboard/tokens
export AGENTLINT_TOKEN=agl_...
agentlint --push
# → Pushed: https://agentlint.sh/dashboard
The token is read from AGENTLINT_TOKEN first, then from ~/.config/agentlint/token (one line, chmod 600 recommended). The token is never read from argv. Push failures (auth, rate limit, network) print one line and exit 0 — the local audit still succeeds. The CLI refuses to send to non-HTTPS URLs (except localhost / 127.0.0.1).
When a --push run is associated with a pull request and the
agentlint GitHub App is installed on
the repo, the App posts a single comment on the PR with the score, a diff
versus the previous run, and links to the dashboard and badge. Subsequent
pushes update the same comment instead of stacking new ones. PR detection
works automatically on GitHub Actions pull_request events; for other CI
vendors set AGENTLINT_PR=<n> or pass --pr <n>.
See packages/cli/README.md for the full security model.
agentlint is agent-built, open to everyone.
The first version of this codebase — the rubric of 30 checks, the score formula, the architecture, the docs, the launch plan — was designed and written by Claude (Anthropic's chat web product) in collaboration with a human product owner. Ongoing maintenance is operated autonomously by Claude Code with a human in the loop for things an agent cannot do: domain purchases, npm 2FA, signing off on public communications, and decisions with legal or financial weight.
This is not a stunt. It's the project's working hypothesis: an AI agent can ship and operate a real piece of developer tooling end-to-end, with real users, real CI, and a public quality bar. agentlint is the test of that hypothesis. It scores 100/100 on its own rubric, ships clean releases, and runs its own CI — the same things it checks in everyone else's repo.
The full operating model is public:
docs/CHARTER.md — the constitution. What the
agent decides alone, what it confirms, what it escalates.docs/PROJECT_STATE.md — what's done,
what's next, updated at the close of every session.docs/PLAYBOOK.md — operational runbooks.docs/DECISIONS.md — append-only log of
architectural and product decisions with rationale.This project is open under MIT to human and agent contributors equally. Open a PR, file an issue, propose a rule — there is no enforcement on who or what wrote the code, only on whether the code passes the bar. The agent reviews and merges contributions like any maintainer.
This repo uses pnpm workspaces. Install pnpm first: npm i -g pnpm or corepack enable.
pnpm install
pnpm run ci # build + typecheck + lint + test + self-audit
pnpm run agentlint . # run agentlint on this repo (eat our own dog food)
Commits in this repo are co-authored by Claude via a
prepare-commit-msg hook. See
CHARTER.md § 6 for the
rationale.
MIT.