Agent Readiness Report

Evaluate how well a repository supports autonomous AI development by analyzing it across nine 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:

1. Clone the repo, scan repository structure, CI configs, and tooling
2. Evaluate 81 criteria across 9 technical pillars
3. Determine maturity level (L1-L5) based on 80% threshold per level
4. Provide prioritized recommendations

Workflow

Step 1: Run Repository Analysis

Execute the analysis script to gather signals from the repository:

bash
1python 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
1python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.json

Step 3: Present Results

The report includes:

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

Nine Technical Pillars

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

See references/criteria.md for the complete list of 81 criteria per pillar.

Five Maturity Levels

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:

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

Common Quick Wins

1. Add AGENTS.md: Document commands, architecture, and workflows for AI agents
2. Configure pre-commit hooks: Catch style issues before CI
3. Add PR/issue templates: Structure task discovery
4. 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.