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:
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:
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 scriptscripts/generate_report.py- Report generation and formattingreferences/criteria.md- Complete criteria definitions by pillarreferences/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.