readiness-report

Assess codebase readiness for autonomous AI by analyzing the repository across eight technical pillars and five maturity levels to identify gaps. This produces a human-readable report and structured JSON analysis with prioritized recommendations for improvement.

AI-agent readiness measures how well a codebase supports autonomous development. It evaluates eight pillars: Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, and Task Discovery across five maturity levels.

Generate a readiness report by triggering the /readiness-report command or running the readiness analysis script against the target repository. The analysis outputs a pillar-by-pillar breakdown with pass/fail statuses and prioritized recommendations.

Yes, the readiness analysis produces a structured JSON analysis alongside a human-readable markdown report. The JSON output validates AI-agent readiness across technical pillars and enables downstream tooling to process the identified gaps programmatically.

Yes, the readiness evaluation can be applied across repositories to assess agent readiness and identify gaps. It evaluates each repository against the eight pillars and five maturity levels to produce comparable pass/fail statuses and recommendations.

The best way to identify gaps is to run a comprehensive repository assessment across eight technical pillars and five maturity levels. This evaluation produces prioritized recommendations, highlighting quick wins and longer-term improvements needed for AI-agent readiness.