Auto Research
Run fast, progressive experiments to map tunable parameters and their effects.
Install
npx skills add dzhng/skills --skill auto-research -g -a claude-code -y- Repository
- dzhng/skills1.4k
- Category
- Writing
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 2,624 tokens
- Runs scripts
- No, instructions only
What it does
Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
Auto Research
Optimize learning per unit of time. Start with one fast, revealing task, plan competing hypotheses, test them separately, combine verified winners, then expand coverage. The durable result is a parameter-effect map plus the best verified artifact. An experiment is a checkpoint, not a stopping point.
Workflow
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Recover the research state. Read the objective, user feedback, project instructions, and existing research records. Reconcile the handoff with actual artifacts and running jobs; collect a pending attempt before launching another. Identify the best verified artifact (the incumbent), active task, regression set, hypothesis queue, and remaining budget. Reuse existing record locations.
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Clarify success before experimenting. Recover any already agreed criteria; do not ask the user to repeat them. If the evaluator, metric, comparison baseline, or required improvement is unclear, ask focused questions and wait for answers before editing candidates or launching evaluations. Inspect existing artifacts and propose concrete options to make answering easier; do not silently choose what success means. Continue clarification until the answers define a checkable criterion, including evaluation scope, aggregation, and non-regression gates. Restate that criterion in the research contract.
