agent-readiness 2.9.0

Benchmark how agent-ready a code repository is for LLM coding agents.

agent-readiness

A benchmark for AI agent readiness of a code repository.

You bought the seats. Your team is using Claude Code, Cursor, Copilot, Cline. And the agents keep going off the rails on your codebase.

The model is the variable you can't change. The repo is what you can.

agent-readiness scans a repository and scores how ready it is for AI coding agents — across cognitive load, feedback loops, and flow — then hands you a prioritised punchlist of fixes. Like Lighthouse, but for AI agent readiness instead of page load.

$ agent-readiness scan .

AI Readiness  62 / 100

  Cognitive load     70 / 100
  Feedback loops     40 / 100   ← biggest drag
  Flow & reliability 75 / 100
  Safety             OK

Top friction (fix these first):
  1. test_command.discoverable — no test invocation found in Makefile,
     package.json, or pyproject.toml
  2. agent_docs.present — no AGENTS.md / CLAUDE.md / .cursorrules at root
  3. headless.no_setup_prompts — README mentions "log in to the dashboard"
     during setup; agents can't traverse this

Every WARN/ERROR finding also carries a paste-ready `fix_prompt` block
(one paragraph of agent-led prose + a verify command) so you can hand it
straight to your coding agent. Pipe `--json` to see them, or use the MCP
server's `list_friction(path)` tool.

Install

pip install agent-readiness

Requires Python 3.11+. From source:

git clone https://github.com/<org>/agent-readiness.git
cd agent-readiness
pip install -e ".[dev]"   # or: make dev

Use from your coding agent

The scanner is also packaged as a Claude Code plugin and a portable Agent Skill. Ask your agent "score this repo for agent readiness" and it scans + fixes via the same scoring engine as the CLI.

The MCP server exposes scan_repo(path) (full scan + score) and list_friction(path) (every WARN/ERROR with its paste-ready fix_prompt, sorted by score_impact desc). Either call returns the same agent-led prose the CLI prints.

Claude Code (recommended): the plugin bundles the MCP server config — no manual JSON paste.

/plugin marketplace add harrydaihaolin/agent-readiness-skill
/plugin install agent-readiness@agent-readiness-skill

Prerequisite: pip install agent-readiness-mcp once.

Cursor / Claude Desktop: clone the skill repo and run its installer for the bare SKILL.md path, then paste the MCP config it prints into your harness:

git clone https://github.com/harrydaihaolin/agent-readiness-skill.git
cd agent-readiness-skill
./scripts/install.sh

See the skill repo for per-harness install details and the community marketplace status.

Design principles

Agents are headless. We assume the agent has stdin / stdout / files / git / HTTP and nothing else. No browser, no dashboard, no clickable button. If important state is reachable only through a UI, it's invisible to the agent — and the repo loses points wherever that's true.

This applies to our own tool, too. agent-readiness is fully headless: no required interactive prompts, stable JSON via --json, exit codes that mean things, machine-readable findings.

Code quality counts only where it predicts agent success. Mega-files, ambiguous names, dead code, missing types — those have direct lines to agent failure modes and get measured. We don't reproduce the full SonarQube taxonomy. Other tools do that well.

Run untrusted code in Docker, always. Any check that executes code from the target repo runs inside a sandboxed container. See docs/SANDBOX.md.

What gets measured

See docs/RUBRIC.md for the full definition. Short version:

Pillar	What it captures
Cognitive load	What the agent must absorb to make a correct change.
Feedback loops	How fast and clear is the signal after a change.
Flow / reliability	Headless walkability + how often friction outside the task blocks the agent.
Safety & trust	Secrets, destructive scripts, gitignore hygiene. (Cap, not weight.)

This repo's score

Dogfooding: agent-readiness scan . run against this repository itself.

╭─────────────────────────────╮
│  AI Readiness  100.0 / 100  │
╰─────────────────────────────╯
 Cognitive load      100.0  ████████████████████
 Feedback loops      100.0  ████████████████████
 Flow & reliability  100.0  ████████████████████
 Safety              100.0  ████████████████████

No findings. Looking good.

Score updated after each iteration as part of the development workflow.

Usage

# Static scan (no Docker needed)
agent-readiness scan .
agent-readiness scan . --json
agent-readiness scan . --fail-below 70        # exit 1 if score < 70 (CI gate)
agent-readiness scan . --only feedback        # filter to one pillar
agent-readiness scan . --baseline prev.json   # diff against a previous run
agent-readiness scan . --report report.html   # HTML report (requires jinja2)
agent-readiness scan . --badge badge.svg      # score badge SVG
agent-readiness scan . --sarif findings.sarif # SARIF for GitHub code scanning

# Runtime scan (executes tests inside Docker)
agent-readiness scan . --run

# Other commands
agent-readiness list-checks
agent-readiness explain manifest.detected
agent-readiness init                          # write .agent-readiness.toml

Status

All phases implemented (v0.1–v0.9). 22 checks across 4 pillars, Docker sandbox, HTML + SARIF renderers, CLI surface, plugin API. See docs/PLAN.md for the full roadmap and CHANGELOG.md for per-phase release notes.

License

MIT for the code; see LICENSE. The project name and logo are governed separately by TRADEMARK.md: forks are welcome, "agent-readiness" the brand is reserved for the canonical project.