Evaluate a codebase for agent-friendliness based on autonomous agent best practices. Use when asked to "evaluate for agents", "check agent readiness", "audit for autonomous execution", "assess agent-friendliness", or when reviewing infrastructure for unattended agent operation. Also use when asked about making a codebase more suitable for AI agents or autonomous workflows.
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See how to installReview the instructions the agent will receive before you install.
Evaluate how well a codebase supports autonomous agent execution based on the "How to Get Out of Your Agent's Way" principles.
Autonomous agents fail for predictable reasons—most are system design failures, not model failures. This evaluation checks whether infrastructure enables true autonomy: agents that run unattended, isolated, reproducible, and bounded by system constraints rather than human intervention.
Explore the codebase for indicators across all 12 principles. Key files to examine:
Environment & Isolation:
Dockerfile, docker-compose.yml, .devcontainer/Makefile, setup.sh, bootstrap.sh.github/workflows/, .gitlab-ci.yml, Jenkinsfile)devbox.json, flake.nixDependencies & State:
package-lock.json, yarn.lock, Pipfile.lock, Cargo.lock, go.sum).env.example, config templatesExecution & Interfaces:
bin/ scriptsQuality & Monitoring:
Read evaluation-criteria.md for detailed scoring rubric.
Score each of the 12 principles 0-3:
Output format:
# Agent-Ready Evaluation Report
**Overall Score: X/36** (Y%)
**Rating: [Excellent|Good|Needs Work|Not Agent-Ready]**
## Summary
[2-3 sentence assessment of overall agent-readiness]
## Principle Scores
| Principle | Score | Evidence |
|-----------|-------|----------|
| 1. Sandbox Everything | X/3 | [brief evidence] |
| 2. No External DB Dependencies | X/3 | [brief evidence] |
| 3. Clean Environment | X/3 | [brief evidence] |
| 4. Session-Independent Execution | X/3 | [brief evidence] |
| 5. Outcome-Based Instructions | X/3 | [brief evidence] |
| 6. Direct Low-Level Interfaces | X/3 | [brief evidence] |
| 7. Minimal Framework Overhead | X/3 | [brief evidence] |
| 8. Explicit State Persistence | X/3 | [brief evidence] |
| 9. Early Benchmarks | X/3 | [brief evidence] |
| 10. Cost Planning | X/3 | [brief evidence] |
| 11. Verifiable Output | X/3 | [brief evidence] |
| 12. Infrastructure-Bounded Permissions | X/3 | [brief evidence] |
## Top 3 Improvements
1. **[Highest impact improvement]**
- Current state: ...
- Recommendation: ...
- Impact: ...
2. **[Second improvement]**
...
3. **[Third improvement]**
...
## Strengths
- [What the codebase does well for agents]
## Detailed Findings
[Optional: deeper analysis of specific areas]
If time is limited, prioritize these high-signal indicators:
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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.
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Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.
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Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.
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Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.
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Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.
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Add new Agent templates to the meta-agent-skills framework.
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