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ab-test-setup

Use before planning or launching any A/B, split or multivariate test, or when asked if a result is significant. Sizes the test with sample_size_calculator.py and writes the hypothesis, metric and stop rule first, so it is not called early on noise.

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable content with executable calculator commands, concrete templates, and explicit checklists, supported by a well-structured bundle of two real reference files and a working script. The main weakness is token efficiency: several sections teach concepts Claude already knows (peeking, p-values, client/server-side basics) and the Common Mistakes section duplicates Core Principles, while the long body inlines content that could be pushed to references.

Suggestions

Trim or remove sections that re-teach known concepts — the 'Statistical Significance' definition (p < 0.05), 'The Peeking Problem' explanation, and the client-side/server-side implementation basics — leaving only the skill-specific prescriptive guidance (don't peek, pre-commit to sample size).

Cut the 'Common Mistakes' section, which restates 'Core Principles' (hypothesis first, single variable, don't stop early), or collapse it into a single short list of analysis-phase-only mistakes like cherry-picking segments.

Move the Quick Reference sample-size table and the Test Types / Implementation sections into references/sample-size-guide.md or a new reference file, keeping the SKILL.md body as a lean overview that points to the calculator command and the references.

DimensionReasoningScore

Conciseness

The body re-explains concepts Claude already knows — "95% confidence = p-value < 0.05; Means <5% chance result is random", the peeking problem definition, and client-side vs server-side implementation basics — and "Common Mistakes" largely restates "Core Principles". It is mostly efficient (tight bullets, tables, task-specific guidance dominates), so it sits at anchor 3 rather than the noticeably-verbose anchor 2, but the several known-concept explanations keep it below 4.

3 / 5

Actionability

Copy-paste-ready commands that match the bundled script's real interface ("python3 scripts/sample_size_calculator.py --baseline 0.05 --mde 0.20 --json"), a fill-in hypothesis template with weak/strong examples, concrete metric tables, and explicit checklists cover the common cases. This matches anchor 5's fully executable, copy-paste-ready standard for an instruction-type skill.

5 / 5

Workflow Clarity

The sequence (assessment → hypothesis → sizing → metrics → variants → launch → analysis) is clear via section order and backed by an explicit pre-launch checklist and a numbered analysis checklist. Not 5 because the ordering is implicit rather than an explicit procedure and there are no error-recovery/feedback loops; not 3 because validation checkpoints are explicit and concrete.

4 / 5

Progressive Disclosure

Two real, clearly signaled one-level-deep references ("For detailed sample size tables and duration calculations: See references/sample-size-guide.md", "For templates: See references/test-templates.md") plus a real script, verified to exist in the bundle. Not 5 because the ~300-line body inlines encyclopedic content (test-type definitions, implementation tool lists, common mistakes) that belongs in reference files; not 3 because the references that do exist are well signaled and appropriately split.

4 / 5

Total

16

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that explicitly states both capabilities and trigger conditions with concrete, tool-anchored actions and good synonym coverage (A/B, split, multivariate). The trigger "when asked if a result is significant" slightly broadens conflict risk, and a few natural terms like "experiment" are missing, but overall it reads like the reference good examples.

DimensionReasoningScore

Specificity

"Sizes the test with sample_size_calculator.py and writes the hypothesis, metric and stop rule first" lists several specific actions with a named tool. Minor gaps in coverage (no analysis-side actions) keep it at anchor 4 rather than 5, and it clearly exceeds the 1-2 concrete actions of anchor 3.

4 / 5

Completeness

It explicitly answers both questions: the what ("Sizes the test with sample_size_calculator.py and writes the hypothesis, metric and stop rule first") and the when ("Use before planning or launching any A/B, split or multivariate test, or when asked if a result is significant") with concrete trigger phrases, matching the anchor-5 example pattern. Not 4 because the when-clause is already fully explicit, not merely adequate.

5 / 5

Trigger Term Quality

"A/B, split or multivariate test" and "asked if a result is significant" cover the key synonyms users naturally say, matching anchor 4's "good keyword coverage; a few natural terms missing". Not 5 because common variations like "experiment", "conversion", and "statistically significant" are absent.

4 / 5

Distinctiveness Conflict Risk

The A/B, split, and multivariate testing niche is distinct with clear triggers, fitting anchor 4's "mostly distinct; minor overlap risk". Not 5 because "asked if a result is significant" is a broad trigger that could also fire for general analytics or statistics skills; not 3 because the domain focus is unmistakable.

4 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

alirezarezvani/claude-skills

Path

marketing-skill/skills/ab-test-setup/SKILL.md

Reviewed

Table of Contents

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