Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
agentic-eval
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
agentic-eval
Best first install candidate based on install readiness and adoption.
Freshest repo
agentic-eval
Most recent maintenance signal among this shortlist.
| Signal | agentic-eval Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality |
|---|---|
| Quality | 92/100 Excellent |
| Decision verdict | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 39K stars Verified outcomes are shown on each skill page |
| Freshness | Sep 1, 2026 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals |
Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
agentic-eval
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
agentic-eval
Best first install candidate based on install readiness and adoption.
Freshest repo
agentic-eval
Most recent maintenance signal among this shortlist.
| Signal | agentic-eval Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality |
|---|---|
| Quality | 92/100 Excellent |
| Decision verdict | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 39K stars Verified outcomes are shown on each skill page |
| Freshness | Sep 1, 2026 |
| Use-case fit | |
| Workflow fit | |
| Platform hints | Claude Code |
| Warnings | No OpenAgentSkill engagement data yet |
| Best for | Coding agents workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add github/awesome-copilot --skill agentic-eval |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies |
| Install | $ npx skills add github/awesome-copilot --skill agentic-eval |