readiness-report

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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.

1installs

Sourceopenhands/extensions

Added on2026-03-06

NPX Install

npx skill4agent add openhands/extensions readiness-report

SKILL.md Content

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:

Clone the repo, scan repository structure, CI configs, and tooling
Evaluate 81 criteria across 9 technical pillars
Determine maturity level (L1-L5) based on 80% threshold per level
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 configurations (CODEOWNERS, .gitignore, secrets management)

Step 2: Generate Report

After analysis, generate the formatted report:

bash

python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.json

Step 3: Present Results

The report includes:

Overall Score: Pass rate percentage and maturity level achieved
Level Progress: Bar showing L1-L5 completion percentages
Strengths: Top-performing pillars with passing criteria
Opportunities: Prioritized list of improvements to implement
Detailed Criteria: Full breakdown by pillar showing each criterion status

Nine Technical Pillars

Each pillar addresses specific failure modes in AI-assisted development:

Pillar	Purpose	Key Signals
Style & Validation	Catch bugs instantly	Linters, formatters, type checkers
Build System	Fast, reliable builds	Build docs, CI speed, automation
Testing	Verify correctness	Unit/integration tests, coverage
Documentation	Guide the agent	AGENTS.md, README, architecture docs
Dev Environment	Reproducible setup	Devcontainer, env templates
Debugging & Observability	Diagnose issues	Logging, tracing, metrics
Security	Protect the codebase	CODEOWNERS, secrets management
Task Discovery	Find work to do	Issue templates, PR templates
Product & Analytics	Error-to-insight loop	Error tracking, product analytics

See

references/criteria.md

for the complete list of 81 criteria per pillar.

Five Maturity Levels

Level	Name	Description	Agent Capability
L1	Initial	Basic version control	Manual assistance only
L2	Managed	Basic CI/CD and testing	Simple, well-defined tasks
L3	Standardized	Production-ready for agents	Routine maintenance
L4	Measured	Comprehensive automation	Complex features
L5	Optimized	Full autonomous capability	End-to-end development

Level Progression: To unlock a level, pass ≥80% of criteria at that level AND all previous levels.

See

references/maturity-levels.md

for detailed level requirements.

Interpreting Results

Pass vs Fail vs Skip

✓ Pass: Criterion met (contributes to score)
✗ Fail: Criterion not met (opportunity for improvement)
— Skip: Not applicable to this repository type (excluded from score)

Priority Order

Fix gaps in this order:

L1-L2 failures: Foundation issues blocking basic agent operation
L3 failures: Production readiness gaps
High-impact L4+ failures: Optimization opportunities

Common Quick Wins

Add AGENTS.md: Document commands, architecture, and workflows for AI agents
Configure pre-commit hooks: Catch style issues before CI
Add PR/issue templates: Structure task discovery
Document single-command setup: Enable fast environment provisioning

Resources

```
scripts/analyze_repo.py
```
- Repository analysis script
```
scripts/generate_report.py
```
- Report generation and formatting
```
references/criteria.md
```
- Complete criteria definitions by pillar
```
references/maturity-levels.md
```
- Detailed level requirements

Automated Remediation

After reviewing the report, common fixes can be automated:

Generate AGENTS.md from repository structure
Add missing issue/PR templates
Configure standard linters and formatters
Set up pre-commit hooks

Ask to "fix readiness gaps" to begin automated remediation of failing criteria.