Individual skill
experiment-craft
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
skills/experiment-craft/SKILL.md
Source commit 4889c2270af5f9a7658bb6a6709bf37040902006
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- Observed behavior
- Instructions only
- Last checked
- Nov 4, 2026
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Repository context
- Repository stars (not skill ratings)
- 488
- Repository last updated
- Oct 27, 2026
- License
- Apache-2.0
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- skills Repository topic
- ai-agent Repository topic
- ai4science Repository topic
- vibe-research Repository topic
- core Declared skill tag
- experimentation Declared skill tag
- experiment-design Declared skill tag
- engineering Derived category
- design Derived category
- marketing Derived category
- writing Derived category
- testing Derived category
- productivity Derived category
- research Derived category
- productivity-notes Derived category
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SKILL.mdcore · 8,038 bytesassets/experiment-log-template.mddoc · 788 bytesreferences/debugging-methodology.mddoc · 6,691 bytes
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# Experiment Craft
A systematic approach to running, debugging, and iterating on research experiments. The critical skill is not running more experiments — it's understanding WHY experiments fail.
## When to Use This Skill
- User's experiment is not working or producing unexpected results
- User needs help diagnosing why a method fails on certain data
- User wants to organize their experiment process with structured logging
- User asks about debugging research code or iterating on approaches
- User mentions "experiment debugging", "why doesn't this work", "experiment log", "results are wrong"
> This skill is typically loaded from within `experiment-pipeline` when a stage attempt fails. After debugging, return to the pipeline's stage-gate structure to continue. Can also be used standalone for any experiment debugging.