AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
agentlint: 코드베이스의 AI 도구 준비도를 0-100점으로 평가하는 무료 로컬 도구
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.
개발 도구TypeScript · JavaScriptGitHub @agentlint온라인운영자 미확인
써 보셨어요? 한 번만 눌러 주세요. 로그인은 필요 없어요.
개인 계정의 공개 저장소 소유자는 GitHub로 로그인해 운영자를 확인하고 소개를 직접 관리할 수 있습니다. 관리자 승인은 필요하지 않습니다.
로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
로그인이 필요하면 로그인 화면으로 이동합니다. 로그인 후 이곳으로 돌아와 원하는 확인 버튼을 다시 눌러 주세요.
프로젝트 폴더에서 /nomorevibe verify를 실행하면 소유권을 확인하고 소개를 직접 관리할 수 있습니다.
1분 만에 확인하기The 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.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
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.README 전문 보기 ↗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.