Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Comparing two prompts on the same test cases
- Checking quality after agent configuration changes
- Turning real failures into repeatable evaluation cases
danielmiessler/LifeOS☆ 2만2026년 9월 4일 업데이트
When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
tjboudreaux/cc-thinking-skills☆ 1.7천2026년 8월 8일 업데이트
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
rohitg00/skillkit☆ 1.6천2026년 6월 2일 업데이트
Systematic debugging methodology - scientific method, hypothesis testing, and root cause analysis that works across all technologiesUse when "bug, debugging, not working, broken, investigate, root cause, why is this happening, figure out, troubleshoot, doesn't work, unexpected behavior, debugging, root-cause, hypothesis, scientific-method, troubleshooting, bug-hunting, investigation, problem-solving" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
omer-metin/skills-for-antigravity☆ 1682026년 1월 22일 업데이트