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dirnbauer
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readiness-report

Analyze a repository across eight technical pillars and five maturity levels.

33|6|Updated Jan 2, 2026
Search Tags:#codebase#report#analyze#readiness#AI-agent#pillar
One-click install
npx skills add https://github.com/dirnbauer/webconsulting-skills --skill readiness-report
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: readiness-report
Source: https://github.com/dirnbauer/webconsulting-skills/tree/main/skills/readiness-report
Command: npx skills add https://github.com/dirnbauer/webconsulting-skills --skill readiness-report

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill analyzes a codebase to gauge readiness for autonomous AI development across eight pillars and five maturity levels, surfacing gaps and actionable improvements.

Core Features & Use Cases

  • Automated readiness analysis across Pillars: Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery, Product & Analytics.
  • Generates structured JSON readiness data and a human-readable report to guide improvements.
  • Use Case: A development team wants to prepare a codebase for AI-assisted workflows and needs a prioritized backlog of fixes.

Quick Start

Run the readiness analysis on the current repo with:

  • python scripts/analyze_repo.py --repo-path .
  • python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.json --format markdown

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?▼

You can assess codebase readiness for autonomous AI by running a Python script that analyzes the repository across eight technical pillars and five maturity levels, outputting a structured JSON file and a human-readable report.

What technical pillars are evaluated when analyzing a repository for AI agent readiness?▼

Analyzing a repository for AI agent readiness evaluates eight technical pillars: style and validation, build system, testing, documentation, dev environment, debugging and observability, security, task discovery, and product analytics.

How do I generate a readable readiness report from my repository analysis?▼

To generate a readability report from repository analysis, run the provided Python report generation script against the output JSON analysis file, specifying the markdown format to produce a human-readable document guiding codebase improvements.

Can I analyze any repository to prepare it for AI-assisted workflows?▼

Yes, you can analyze any repository where agents will operate, especially for code interpretation, automation, and runtime decision-making, to produce a prioritized backlog of fixes preparing the codebase for AI-assisted workflows.

What output formats does the codebase readiness analysis produce?▼

The codebase readiness analysis produces a structured JSON file named readiness_analysis.json containing the raw evaluation data, and a human-readable markdown report for guiding improvements.

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