readiness-report

Analyze repository structure, CI, tooling, and documentation to quantify AI-readiness gaps across nine pillars.

317|108|Updated Oct 18, 2025

Search Tags:#documentation #codebase #analysis #reporting #ai-agent #readiness #maturity

Ready to run

npx skills add https://github.com/AojdevStudio/Finance-Guru --skill readiness-report-aojdevstudio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: readiness-report
Source: https://github.com/AojdevStudio/Finance-Guru/tree/main/.agents/skills/readiness-report
Command: npx skills add https://github.com/AojdevStudio/Finance-Guru --skill readiness-report-aojdevstudio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Evaluate and quantify how well a codebase supports autonomous AI development by analyzing repository signals, CI configurations, tooling, and documentation to surface readiness gaps.

Core Features & Use Cases

Analyzes repositories across nine technical pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery, Product & Analytics) and five maturity levels.
Produces a prioritized readiness report that informs agent deployment, codebase improvements, and project planning.
Useful for assessing agent readiness, codebase maturity, and identifying gaps preventing AI-assisted development.

Quick Start

Run the readiness report on your repository to generate a full analysis.

Frequently Asked Questions about readiness-report

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

FAQPage Schema

How do I assess my codebase readiness for autonomous AI agents?▼

Assess codebase AI readiness by analyzing repository structure, CI configurations, tooling, and documentation across nine technical pillars to quantify maturity gaps and produce a prioritized readiness report.

What is codebase AI maturity and how is it measured?▼

Codebase AI maturity measures how well a repository supports autonomous AI development, evaluated across nine pillars and five levels using an 80% pass-rate threshold per level to assign an achieved maturity score.

Can I evaluate a monorepo for AI agent readiness and task discovery?▼

Yes, you can evaluate any software project or monorepo where agents operate, analyzing signals across nine pillars including Task Discovery, Security, and Documentation to surface readiness gaps.

How do I generate a machine-readable report of codebase readiness gaps?▼

Generate a readiness report by running the analysis on your repository, which outputs both human-readable and machine-readable formats detailing pass rates, maturity levels, and prioritized actions.

What are the limitations of automated codebase readiness analysis?▼

The analysis relies on repository signals, CI configurations, and documentation presence, meaning it evaluates structural and tooling maturity rather than subjective code quality or complex business logic suitability.