experiment-design
experiment-design is an Experimentation & statistics skill for AI agents, published by beita6969 in the ScienceClaw repository on GitHub. Skillspace rates experiment-design D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Design scientific experiments including sample size calculation, randomization, control groups, blinding, and study protocols. Covers RCTs, quasi-experiments, factorial designs, A/B tests, survey design, and observational studies. Use when user asks to design an experiment, calculate sample size, plan a study, set up controls, or create a research protocol. Triggers on "design experiment", "sample size", "power analysis", "study design", "control group", "randomization", "A/B test", "factorial design", "survey design".
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 866
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 97 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 1 other repositories, in 1 collections
- Scope
- Portable: works in any project. Generic scientific experiment design and power-analysis methodology
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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