readiness-report
Evaluate how well a codebase supports autonomous AI development. Analyzes repositories across eight technical pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery) and five maturity levels. Use when users request `/readiness-report` or want to assess agent readiness, codebase maturity, or identify gaps preventing effective AI-assisted development.
npx skills add majiayu000/claude-skill-registry --skill readiness-report --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agent Readiness Report Evaluate how well a repository supports autonomous AI development by analyzing it across eight technical pillars and five maturity levels. ## Overview Agent Readiness measures how prepared a codebase is for AI-assisted development. Poor feedback loops, missing documentation, or lack of tooling cause agents to waste cycles on preventable errors. This skill identifies those gaps and prioritizes fixes. ## Quick Start The user will run `/readiness-report` to evaluate the current repository. The agent will then: 1. Clone the repo, scan repository structure, CI configs, and tooling 2. Evaluate 81 criteria across 9 technical pillars 3. Determine maturity level (L1-L5) based on 80% threshold per level 4. Provide prioritized recommendations ## Workflow ### Step 1: Run Repository Analysis Execute the analysis script to gather signals from the repository: ```bash python scripts/analyze_repo.py --repo-path . ``` This script checks for: - Configuration files (.eslintrc, pyproject.toml, etc.) - CI/CD workflows (.github/workflows/, .gitlab-ci.yml) - Documentation (README, AGENTS.md, CONTRIBUTING.md) - Test infrastructure (test directories, coverage configs) - Security con
- Overview
- Quick Start
- Workflow
- Step 1: Run Repository Analysis
- Step 2: Generate Report
- Step 3: Present Results
- Nine Technical Pillars
- Five Maturity Levels
- Interpreting Results
- Pass vs Fail vs Skip
- Priority Order
- Common Quick Wins
- Resources
- Automated Remediation
python scripts/analyze_repo.py --repo-path . python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.json
What does the readiness-report skill do?
Evaluate how well a codebase supports autonomous AI development. Analyzes repositories across eight technical pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery) and five maturity levels. Use when users request `/readiness-report` or want to assess agent readiness, codebase maturity, or identify gaps preventing effective AI-assisted development.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill readiness-report --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.