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Designing Experiments

foryourhealth111-pixel/designing-experiments

Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.

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on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill designing-experiments

The instruction itself

4 sections, as written by the author

Designing Experiments

Helps choose and specify a research design before data analysis starts. This skill owns study-design decisions: what is treated, what is compared, what outcome is measured, which assumptions are required, which validation or recovery experiment should follow a failed scientific experiment, and which design is defensible.

It does not fit causal models, estimate treatment effects, interpret fitted model output from existing data, or debug software/build failures.

Decision Framework

  • Control Group?
  • Yes: Go to Step 2.
  • No: Consider Interrupted Time Series (ITS).
  • Unit Structure?
  • Single Treated Unit:
  • With multiple controls: Synthetic Control (SC).
  • No controls: ITS.
  • Multiple Treated Units:
  • With control group: Difference-in-Differences (DiD).
  • Time Structure?
  • Panel Data (Multiple units over time): Required for DiD and SC.
  • Time Series (Single unit over time): Required for ITS.

Method Quick Reference

  • Difference-in-Differences (DiD): Compares trend changes between treated and control groups. Assumes Parallel Trends.
  • Interrupted Time Series (ITS): Analyzes trend/level change for a single unit after intervention. Assumes Trend Continuity.
  • Synthetic Control (SC): Constructs a synthetic counterfactual from weighted control units. Assumes Convex Hull (treated unit within range of controls).

Failed Experiment Recovery

When a scientific experiment or optimization plan produces weak or contradictory results, use the same design surface to:

  • Separate implementation or measurement errors from design-assumption failures.
  • Identify which assumption should be tested next.
  • Define a minimal validation experiment before abandoning the approach.
  • State the decision rule for continuing, revising, or stopping the line of work.

How to use it

Copy the folder

Take foryourhealth111-pixel/designing-experiments from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.