The description mentions 'plan, design, or implement an A/B test or experiment' but these are very high-level actions without concrete specifics. It doesn't list what the skill actually does—no specific deliverables like 'create hypothesis documents,' 'calculate sample sizes,' 'generate variant copy,' or 'analyze test results.'

The 'when' is explicitly and thoroughly covered with a 'Use when' clause and trigger terms. However, the 'what' is weak—it says 'plan, design, or implement' but doesn't describe what concrete outputs or actions the skill performs. The description is almost entirely trigger-focused with minimal capability description.

Excellent coverage of natural trigger terms: 'A/B test,' 'split test,' 'experiment,' 'test this change,' 'variant copy,' 'multivariate test,' 'hypothesis.' These are terms users would naturally use when requesting help with experimentation.

The description carves out a clear niche around A/B testing and experimentation, and even explicitly delineates boundaries by directing tracking implementation to 'analytics-tracking.' The trigger terms are specific to this domain and unlikely to conflict with other skills.

The skill is reasonably well-organized but includes some unnecessary explanations Claude already knows (e.g., explaining what statistical significance means, what p-values are, the peeking problem). Some sections like 'Core Principles' state obvious experimentation concepts. The tables and quick references are efficient, but overall it could be tightened by ~30%.

The skill provides structured frameworks (hypothesis template, checklists, tables) which are useful, but lacks executable code or concrete implementation examples. There are no code snippets for setting up tracking, configuring tools, or implementing variants. The guidance is more conceptual/procedural than copy-paste ready, though the hypothesis example and metrics examples add some concreteness.

The skill presents a clear sequential workflow from initial assessment through hypothesis formation, test design, implementation, running, and analysis. The pre-launch checklist with checkboxes, DO/DON'T lists during testing, and the analysis checklist provide explicit validation checkpoints. The flow is logical and well-sequenced with clear decision points.

The skill references two external files (references/sample-size-guide.md and references/test-templates.md) and related skills, which is good structure. However, no bundle files are provided, so these references are broken. Additionally, the main file is quite long (~200+ lines) with content like the full sample size table and common mistakes that could be split into reference files to keep the overview leaner.