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experiment-designer

Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.

74

Quality

91%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

90%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.

A well-organized, lean skill body with executable tooling and a clear six-step workflow. Minor gaps are an absent explicit error-recovery feedback loop and generic rather than topic-labeled reference navigation.

Suggestions

Add an explicit validation/feedback loop to the Core Workflow (e.g., after launch: monitor guardrails → if breached, pause and re-plan → re-run with corrected setup) to push workflow clarity higher.

Replace the plain 'See:' filename list with topic-anchored navigation labels, e.g. '**Experiment setup & playbooks**: See references/experiment-playbook.md' and '**Statistics concepts**: See references/statistics-reference.md'.

Add one or two natural trigger synonyms to the description (e.g. 'A/B testing', 'split test', 'experiment readout') to broaden trigger-term coverage.

DimensionReasoningScore

Conciseness

The body is lean and well-sectioned, assuming Claude's competence without explaining what A/B tests, p-values, or confidence intervals are; every section earns its place.

5 / 5

Actionability

It provides two copy-paste-ready, fully-argumented sample-size bash invocations backed by a complete, runnable calculator script, plus a concrete If/Then/Because template and the ICE formula.

5 / 5

Workflow Clarity

The six-step Core Workflow is clearly sequenced with stopping rules and guardrail monitoring as checkpoints, but it lacks an explicit validate-then-fix-retry feedback loop for the batch user-facing operations involved.

4 / 5

Progressive Disclosure

SKILL.md is a concise overview that signals one-level-deep references to two real, appropriately-split reference files plus a documented script, though the 'See:' navigation uses generic filenames rather than topic-anchored labels.

4 / 5

Total

18

/

20

Passed

Description

92%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, concise description that leads with an explicit trigger clause and enumerates five concrete, well-scoped capabilities. Trigger-term coverage is the only slight gap, missing a few natural synonyms.

DimensionReasoningScore

Specificity

The description lists five concrete, distinct actions ('planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes'), giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

It opens with an explicit 'Use when...' trigger clause (the when) whose enumerated actions also define the what, clearly and explicitly answering both with concrete trigger phrases.

5 / 5

Trigger Term Quality

It surfaces natural terms users would say ('A/B outcomes', 'hypotheses', 'sample size', 'prioritizing tests', 'product experiments'), but is missing some common synonyms and variations a user might naturally voice.

4 / 5

Distinctiveness Conflict Risk

The product-experimentation / A/B-statistics framing carves a clear niche with distinct triggers and minimal overlap risk against generic data or coding skills.

5 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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