The content is generally efficient but includes some unnecessary framing ('You are an expert...') and explanatory text that Claude already knows (e.g., explaining what statistical significance means, what peeking is). The tables and frameworks are well-structured but could be tighter.

Provides concrete, actionable guidance throughout: specific hypothesis templates, sample size tables with exact numbers, implementation checklists, and clear decision frameworks. The examples are specific and the tables provide copy-paste ready reference material.

Clear sequential workflow from hypothesis through analysis with explicit checkpoints: pre-launch checklist, during-test DO/DON'T lists, and analysis checklist. The process is well-sequenced with validation steps (QA, tracking verification) before proceeding.

Well-organized with clear sections, appropriate use of tables for reference data, and explicit links to deeper content (sample-size-guide.md, test-templates.md). The skill serves as an effective overview with one-level-deep references to detailed materials.

Names the domain (A/B testing/experiments) and mentions actions like 'plan, design, or implement,' but doesn't list specific concrete capabilities like 'create test variants, calculate sample sizes, analyze results.'

Clearly answers both what (plan, design, implement A/B tests/experiments) and when (explicit 'Use when' equivalent at the start plus extensive trigger terms). Also includes helpful disambiguation pointing to analytics-tracking for related but distinct needs.

Excellent coverage of natural terms users would say: 'A/B test,' 'split test,' 'experiment,' 'test this change,' 'variant copy,' 'multivariate test,' 'hypothesis' - these are all terms users naturally use when needing this skill.

Clear niche with distinct triggers specific to experimentation. The explicit disambiguation ('For tracking implementation, see analytics-tracking') actively reduces conflict risk with related skills.