mohitagw15856
05b6d799f0
Three more learnings from alirezarezvani/claude-skills, applied: 1. SkillCheck validator (scripts/skillcheck.mjs) — validates every SKILL.md against the authoring standard (frontmatter, name/folder match, trigger + produces clauses, required headings) plus tier referential integrity. Errors fail CI; --strict fails on warnings too. New skillcheck.yml workflow and a SkillCheck status badge in the README. Current: 0 errors / 14 advisory warnings across 172 skills. 2. Cursor export platform — build-exports.mjs now generates exports/cursor/<bundle>/<skill>/<skill>.mdc rule files. The PLATFORMS registry now supports per-skill filenames (file as a function). 3. Per-agent installers — scripts/install.sh unifies install for claude/hermes/codex/openclaw/cursor (--link, --target, --dry-run, --list). Curl-able one-liners codex-install.sh, openclaw-install.sh, and cursor-install.sh clone the library and install in a single command. README documents the one-line installs and Cursor exports; CHANGELOG and the authoring standard updated. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
79 lines
4.0 KiB
Plaintext
79 lines
4.0 KiB
Plaintext
---
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description: "Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation."
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globs:
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alwaysApply: false
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---
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# Experiment Designer Skill
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Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist.
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## Required Inputs
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Ask the user for these if not provided:
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**For experiment design:**
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- Hypothesis (what change, what metric, what expected movement)
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- Current baseline metric value
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- Minimum detectable effect (MDE) — the smallest lift worth caring about
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- Available daily sample size
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**For results interpretation:**
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- Control and variant results (raw numbers or percentages)
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- P-value or confidence interval
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- Run duration (days)
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- Any anomalies observed during the test
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## Two-Phase Process
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### Phase 1: Experiment Design
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1. Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]"
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2. Define control and variant clearly
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3. Select primary metric (one only) and secondary guardrail metrics (2-3 max)
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4. Calculate required sample size from MDE and baseline
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5. Estimate run time in days
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6. Set pre-defined success criteria before the test runs — no moving goalposts
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7. Flag design risks: novelty effects, seasonal confounds, multiple testing issues, network effects, sample ratio mismatch
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### Phase 2: Results Interpretation
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1. Assess statistical significance (p < 0.05 threshold)
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2. Assess practical significance: was the lift meaningful for the business, not just real?
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3. Interpret confidence intervals
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4. Investigate confounding factors
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5. Recommend: Ship / Iterate / Kill / Run follow-up test
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6. **Validate** — Confirm the test ran for the full planned duration. Flag if it was stopped early (peeking problem). Confirm sample ratio mismatch did not occur.
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## Output Structure
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**[Design or Results header based on phase]**
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*Hypothesis:* "If we [change], we expect [metric] to [move by X%] because [reason]"
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*Primary metric:* [One metric only]
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*Guardrail metrics:* [2-3 max]
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*Required sample size:* [n per variant]
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*Estimated run time:* [days]
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*Pre-defined success threshold:* [specific number]
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*Design risk flags:* [any concerns]
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**Results (Phase 2 only):**
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*Statistical significance:* [p-value and conclusion]
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*Practical significance:* [lift size vs. business threshold]
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*Recommendation:* Ship / Iterate / Kill / Follow-up — [rationale]
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## Quality Checks
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- [ ] Hypothesis specifies the change, the metric, the direction, and the reason
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- [ ] Primary metric is singular — guardrail metrics are secondary
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- [ ] Success criteria are defined before the test launches (not after seeing results)
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- [ ] Test was not stopped early (or flagged clearly if it was)
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- [ ] Practical significance assessed separately from statistical significance
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- [ ] Sample ratio mismatch is checked in results interpretation
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## Anti-Patterns
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- [ ] Do not define success criteria after seeing preliminary results — post-hoc success definitions are HARKing (Hypothesising After Results are Known) and invalidate the experiment
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- [ ] Do not stop a test early because the result looks significant — early stopping dramatically inflates false positive rates; the test must run to the planned sample size
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- [ ] Do not treat statistical significance as the same as practical significance — a p < 0.05 result with a 0.1% lift is real but may not be worth shipping
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- [ ] Do not run the same experiment on the same population multiple times without correction — multiple testing inflates the chance of a false positive proportionally
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- [ ] Do not use more than one primary metric — multiple primary metrics require multiple hypothesis corrections and make the ship/kill decision ambiguous
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