GitHub Action that scores how ready a repository is for AI coding agents and checks its agent instruction files. Deterministic, no LLM.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/alipajand/agent-readiness-action && cp agent-readiness-action/*.md ~/.claude/agents/Resumen de Subagents
# agent-readiness-action
[](https://github.com/alipajand/agent-readiness-action/releases)
[](https://github.com/alipajand/agent-readiness-action/actions/workflows/ci.yml)
[](https://github.com/alipajand/agent-readiness-action/actions/workflows/ci.yml)
[](https://github.com/alipajand/agent-readiness-action/actions/workflows/ci.yml)
[](https://github.com/alipajand/agent-readiness-action/actions/workflows/codeql.yml)
[](LICENSE)
## What it is
A GitHub Action that checks how ready a repository is for AI coding agents (Claude Code,
Cursor, Codex, Copilot, and similar). It bundles two deterministic engines:
- [agent-readiness-kit](https://github.com/alipajand/agent-readiness-kit) scores the
repository from 0 to 100: instruction files, architecture notes, scripts, tests, safety
boundaries, and more. Lockfiles, version pins, test runners, linters, and formatters are
scored on the files of each detected ecosystem (Node.js, Python, Go, Rust). It prints a
category breakdown in the log and job summary.
- [agent-context-doctor](https://github.com/alipajand/agent-context-doctor), with
`context-audit: 'true'`, checks the instruction files themselves: placeholders, risky
directives, contradictions, stale commands, pasted secrets, hidden Unicode, and risky
Claude Code and MCP configuration such as `bypassPermissions`, hooks that run remote
scripts, or third-party API endpoints.
It can fail the build below a score, block pull requests that lower the score, write a
Markdown report, and post a summary comment on pull requests.
Both engines are bundled into the action at pinned commits, so nothing is downloaded or
executed from a package registry at run time. There are no external API calls (other than
the optional PR comment), no telemetry, and no LLM calls.
## Why use it
- Catch missing or low-quality agent context (instruction files, architecture notes,
setup docs) before it slows down day-to-day agent work.
- Catch instruction files and agent settings that steer agents toward unsafe changes or
let them run commands without asking.
- Track the readiness score over time and gate pull requests on a minimum score or on
any drop from the base branch.
- Give reviewers a concise, deterministic summary on each PR — a complement to human
review, not a replacement for it.
## Quick start
```yaml
# .github/workflows/agent-readiness.yml
name: Agent Readiness Audit
on:
pull_request:
push:
branches: [main]
permissions:
contents: read
jobs:
audit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: alipajand/agent-readiness-action@v1
```
## Examples
### Basic audit
```yaml
name: Agent Readiness Audit
on:
pull_request:
push:
branches: [main]
permissions:
contents: read
jobs:
audit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: alipajand/agent-readiness-action@v1
```
### Fail below a minimum score
```yaml
- uses: alipajand/agent-readiness-action@v1
with:
min-score: '70'
fail-on-threshold: 'true'
```
### Comment on pull requests
```yaml
permissions:
contents: read
pull-requests: write
issues: write
steps:
- uses: actions/checkout@v7
- uses: alipajand/agent-readiness-action@v1
with:
comment-on-pr: 'true'
min-score: '70'
github-token: ${{ github.token }}
```
The action posts a single comment the first time and updates it on subsequent runs, so
there are no duplicate comments. It only updates a comment that starts with its hidden
marker and was written by a bot account, so pasting the marker into your own comment
does not make the action overwrite it. When commenting with a personal access token,
set `comment-author` to that account's login.
### Block pull requests that lower the score
```yaml
on:
pull_request:
jobs:
audit:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
with:
fetch-depth: 0 # the base commit must be available
- uses: alipajand/agent-readiness-action@v1
with:
baseline-ref: ${{ github.event.pull_request.base.sha }}
max-score-drop: '0'
```
The action audits the base commit in a temporary git worktree, reports the change (`▼ -5 vs <sha>`) in the log, job summary, and PR comment, and fails when the score drops by more than `max-score-drop`.
### Also check the agent instruction files
```yaml
- uses: alipajand/agent-readiness-action@v1
with:
context-audit: 'true' # run the bundled agent-context-doctor
context-fail-on: 'high' # fail on risky instructions, secrets, hidden characters, ...
```
With `context-audit`, the action also runs [agent-context-doctor](https://github.com/alipajand/agent-context-doctor) on the same path. Its score and top issues appear in the log, job summary, and PR comment. It honors the audited repository's `.acdrc`.
### Write a Markdown report artifact
```yaml
steps:
- uses: actions/checkout@v7
- uses: alipajand/agent-readiness-action@v1
with:
output: 'docs/agent-readiness-report.md'
- uses: actions/upload-artifact@v7
with:
name: agent-readiness-report
path: docs/agent-readiness-report.md
```
### Audit a subdirectory or monorepo package
```yaml
- uses: alipajand/agent-readiness-action@v1
with:
repo-path: './packages/web'
```
## Inputs
| Input | Default | Description |
| ------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `repo-path` | `.` | Path to the repository or subdirectory to audit. Relative paths are resolved from the GitHub Actions workspace root. |
| `min-score` | `0` | Minimum acceptable score (0–100). Checked when `fail-on-threshold` is `true`. |
| `output` | `''` | Write a Markdown report to this path. Relative paths are resolved under `repo-path`, and the result must stay inside `repo-path`. The report is never written through a symlink. |
| `json` | `false` | Echo the raw JSON audit output to the Actions log. |
| `comment-on-pr` | `false` | Post or update a PR comment. Only runs on `pull_request` events. Requires a token with `pull-requests: write` and `issues: write`. |
| `github-token` | `''` | Token for the PR comment, usually `${{ github.token }}`. Falls back to the `GITHUB_TOKEN` environment variable. |
| `comment-author` | `''` | Only update an earlier summary comment written by this login. Defaults to any bot account. |
| `baseline-ref` | `''` | Git ref to compare against, usually `${{ github.event.pull_request.base.sha }}`. Audited in a temporary worktree; needs `fetch-depth: 0`. |
| `max-score-drop` | `''` | Fail when the score is more than this many points below the `baseline-ref` score (`0` fails on any drop). Requires `baseline-ref`. |
| `job-summary` | `true` | Write the audit summary to the workflow run's job summary. |
| `context-audit` | `false` | Also check agent instruction files with the bundled agent-context-doctor. |
| `context-fail-on` | `''` | Fail when agent-context-doctor finds an issue at or above `low`, `medium`, or `high`. Requires `context-audit`. |
| `fail-on-threshold` | `true` | Fail the step when the score is below `min-score`. |
## Outputs
| Output | Description |
| --------------------- | --------------------------------------------------------------------------------- |
| `score` | Final agent-readiness score (0–100). |
| `report-path` | Absolute path of the written Markdown report, or empty when `output` was not set. |
| `passed` | `"true"` when the score is at least `min-score`, otherwise `"false"`. |
| `categories` | JSON array of category scores: `id`, `label`, `score`, `maxScore`. |
| `baseline-score` | Score at `baseline-ref` (only when `baseline-ref` is set). |
| `score-delta` | Score minus the baseline score (only when `baseline-ref` is set). |
| `cLo que la gente pregunta sobre agent-readiness-action
¿Qué es alipajand/agent-readiness-action?
+
alipajand/agent-readiness-action es subagents para el ecosistema de Claude AI. GitHub Action that scores how ready a repository is for AI coding agents and checks its agent instruction files. Deterministic, no LLM. Tiene 1 estrellas en GitHub y su última actualización registrada es del 2026-10-13.
¿Cómo se instala agent-readiness-action?
+
Puedes instalar agent-readiness-action clonando el repositorio (https://github.com/alipajand/agent-readiness-action) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar alipajand/agent-readiness-action?
+
Nuestro agente de seguridad ha analizado alipajand/agent-readiness-action y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene alipajand/agent-readiness-action?
+
alipajand/agent-readiness-action es mantenido por alipajand. La última actividad registrada en GitHub es del 2026-10-13, con 1 issues abiertos.
¿Hay alternativas a agent-readiness-action?
+
Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
Despliega agent-readiness-action en tu cloud
Lleva este repo a producción en minutos. Cada plataforma genera su propio entorno con variables de entorno editables.
¿Mantienes este repo? Añade un badge a tu README
Pega el badge en tu README de GitHub para mostrar que está auditado por ClaudeWave. Cada badge enlaza de vuelta a esta página y muestra el Trust Score actual.
[](https://claudewave.com/repo/alipajand-agent-readiness-action)<a href="https://claudewave.com/repo/alipajand-agent-readiness-action"><img src="https://claudewave.com/api/badge/alipajand-agent-readiness-action" alt="Featured on ClaudeWave: alipajand/agent-readiness-action" width="320" height="64" /></a>Más Subagents
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
The agent that grows with you
Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.
Java 面试 & 后端通用面试指南,覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.
The agent engineering platform.