Lists multiple specific concrete actions and techniques: SQL-based assignment, exposure logging, metric definitions in dbt models, statistical analysis, CUPED variance reduction, sequential testing, and operational tradeoffs vs named platforms (Statsig, Optimizely).

Clearly answers both 'what' (SQL-based assignment, exposure logging, metric definitions, statistical analysis, CUPED, sequential testing, platform tradeoffs) and 'when' with explicit triggers ('Triggers on warehouse-native experimentation...', 'Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle').

Excellent coverage of natural terms users would say: 'warehouse-native experimentation', 'run experiments in BigQuery', 'run experiments in Snowflake', 'dbt experiments', 'SQL t-test', 'CUPED', 'exposure log', 'sample ratio mismatch', 'sequential testing', 'mSPRT', 'build vs buy experimentation'. These are highly specific and natural terms a practitioner would use.

Occupies a very clear niche: warehouse-native experimentation as opposed to platform-based experimentation. The specific mention of BigQuery, Snowflake, dbt, and named platforms like Statsig and Optimizely makes it highly distinguishable from general data analysis, general A/B testing, or general SQL skills.

The skill is extremely verbose at ~400+ lines. It explains concepts Claude already knows (what platforms are, what dbt does, what a t-test is), includes lengthy prose justifications for decisions, and repeats the same points multiple times (e.g., exposure logging discipline is explained in the architecture section, the exposure log section, the pitfalls section, and the framework checklist). The 'when warehouse-native is the right call' section reads like a blog post, not an instruction set.

The skill provides fully executable SQL and Python code throughout: deterministic hash assignment in SQL, exposure log schema, dbt metric model patterns, a complete Welch's t-test in SQL, Python t-test with scipy, CUPED implementation, and power analysis with statsmodels. Code examples are copy-paste ready with realistic field names and patterns.

The 12-consideration framework provides a clear sequence, and the data flow architecture (assignment → exposure → metrics → analysis) is well-articulated. However, there are no explicit validation checkpoints or feedback loops in the workflow steps. The SRM check is mentioned as a consideration but not integrated as a mandatory gate before analysis. The pitfalls section lists problems but doesn't embed verification steps into the workflow itself.

The skill references 8 separate reference files with clear descriptions and links, which is good structure. However, the main SKILL.md itself contains enormous amounts of detail that should be in those reference files (full SQL templates, full Python examples, lengthy prose on when to use warehouse-native). The body should be a concise overview pointing to the references, but instead it duplicates much of what the references presumably contain. Additionally, no bundle files were provided, so the references are unverifiable.