Experiment Designer
v2.1.1Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical s...
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byAlireza Rezvani@alirezarezvani
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (experiment design, hypothesis writing, sample-size estimation) match the included materials: two reference docs and a local sample-size calculator script. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md stays on-topic (hypothesis format, metrics, sample-size estimation, ICE prioritization, stopping rules). The instructions only reference local files included in the package and show how to run the local Python script; they do not direct the agent to read unrelated files or transmit data externally.
Install Mechanism
No install spec is present (instruction-only skill with one local script). Nothing is downloaded or extracted from external URLs and no packages are installed automatically.
Credentials
The skill requires no environment variables, no credentials, and no config paths. All functionality is local and proportional to the stated purpose.
Persistence & Privilege
always is false and the skill is user-invocable. It does not request persistent system-wide changes or elevated privileges.
Assessment
This skill appears to be what it claims: documentation plus a local Python sample-size calculator. Before using: (1) review the sample_size_calculator.py to ensure its assumptions (two-proportion A/B, equal group sizes, interpretation of relative vs absolute MDE) match your experiment; (2) validate results against another calculator or statistical package when stakes are high; and (3) remember this tool does not handle sequential monitoring, multiple comparisons, or continuous-metric power analyses — apply appropriate statistical corrections in your workflow.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
