Lists multiple specific concrete topics: confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and interpretation failures. These are highly specific statistical and experimentation concepts.

The 'what' is well-covered with the list of statistical concepts and the framing of 'how to read experiment results without fooling yourself.' However, there is no explicit 'Use when...' clause or equivalent trigger guidance telling Claude when to select this skill, which caps this at 2 per the rubric guidelines.

Excellent coverage of natural terms users would say when seeking help with experiment analysis: 'experiment results', 'confidence intervals', 'p-values', 'multiple testing', 'CUPED', 'A/B testing' is implied through 'shipping decisions', 'treatment effects', 'ratio metrics', 'network effects'. These are terms practitioners naturally use.

Highly distinctive niche focused specifically on A/B testing and experiment interpretation. The combination of specific statistical terms like CUPED, sequential testing, heterogeneous treatment effects, and the framing around 'shipping decisions' makes this very unlikely to conflict with general statistics or data analysis skills.

Extremely verbose at ~3500+ words. Extensively explains statistical concepts Claude already knows (what a p-value is, what a CI means, SUTVA definition, delta method explanation). The introductory paragraphs, 'what this skill is for' section, and closing section are largely meta-commentary that doesn't add actionable value. Much of this reads like a statistics textbook rather than a concise skill reference.

Provides concrete decision rules (the 5 CI interpretation rules are genuinely useful) and specific platform names, but contains zero executable code, no concrete commands, no worked numerical examples inline, and no copy-paste-ready artifacts. The guidance is specific in places (e.g., 'never report as X, always report as Y') but mostly descriptive rather than executable.

The 14-consideration framework provides a clear checklist structure, and the CI decision rules are well-sequenced. However, there is no explicit step-by-step workflow for reading a result panel (e.g., 'Step 1: check allocation, Step 2: read CI, Step 3: check guardrails'). The content describes what to think about but doesn't sequence the actual interpretation process with validation checkpoints.

References to 7 supporting files are well-organized in a dedicated section with clear descriptions, and inline references are appropriately placed. However, the main SKILL.md itself is monolithic — much of the detailed statistical explanation (CUPED mechanics, delta method details, Bayesian vs frequentist comparison) should be in the reference files rather than inline, and no bundle files were provided to verify the references exist.