readiness-report

Name: readiness-report
Availability: InStock
Author: OpenHands

Analyze codebases across eight pillars and five maturity levels to produce JSON and markdown readiness reports.

134|67|Updated Nov 24, 2025

Search Tags:#documentation #CI/CD #codebase #AI #analyze #readiness #pillars

Ready to run

npx skills add https://github.com/OpenHands/skills --skill readiness-report-openhands

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: readiness-report
Source: https://github.com/OpenHands/skills/tree/main/skills/readiness-report
Command: npx skills add https://github.com/OpenHands/skills --skill readiness-report-openhands

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill evaluates how well a codebase supports autonomous AI development by analyzing it across eight pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery) and five maturity levels. It can be activated when users request /readiness-report or want to assess agent readiness and identify gaps preventing effective AI-assisted development.

Core Features & Use Cases

Comprehensive repository assessment across the nine pillars and five maturity levels to determine readiness.
Prioritized recommendations for improving AI-agent readiness, including quick wins and longer-term improvements.
Automated reporting: produces human-readable markdown reports and structured JSON analysis for downstream tooling.

Quick Start

Trigger the readiness evaluation by issuing the /readiness-report command in your workflow or run the readiness analysis script against the target repository.

Frequently Asked Questions about readiness-report

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I assess if my codebase is ready for autonomous AI agents?▼

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.

What is AI-agent readiness and what technical pillars are evaluated?▼

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.

How do I generate a readiness report for my repository?▼

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.

Can I get a structured JSON analysis of my codebase gaps for downstream tooling?▼

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.

Does the readiness evaluation work across multiple repositories?▼

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.

What is the best way to identify gaps preventing effective AI-assisted development?▼

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.