The body is largely expert-level and not padded with basics Claude already knows, but at ~28KB it carries redundancy: the '12 considerations' framework recapitulates the dedicated sections (assignment, exposure, power, CUPED, sequential testing, multiple comparisons), the closing rhetorical prose restates the 'when it is the right call' section, and the intro overlaps 'What this skill is for' — so it 'could be tightened' rather than earning 'every token earns its place.'

It provides multiple fully executable, copy-paste-ready blocks — BigQuery hash assignment SQL, a complete Welch's t-test SQL CTE, a dbt metric model, scipy/statsmodels Python for the t-test, CUPED, and power analysis — plus a concrete exposure-event schema table; this matches 'fully executable code/commands; copy-paste ready' (the CUPED `pre_period_revenue()` helper is the only mildly illustrative line).

It offers a clearly sequenced 4-component architecture (Assignment → Exposure logging → Metric definitions → Analysis), an explicit 12-step design checklist, and concrete validation checkpoints with feedback loops ("Check the SRM before computing any metric," audit-before-celebrating, pre-register stop criteria, pitfall → diagnose → fix), satisfying 'clear sequence with explicit validation steps; feedback loops; checklists.'

The body is an overview that signals one-level-deep references inline ("Detail in [`references/...`]" at each section) and summarizes all 8 in a dedicated 'Reference files' section; every referenced file exists in references/, content is appropriately split, and navigation is easy — matching 'clear overview with well-signaled one-level-deep references.'

It lists multiple specific concrete capabilities — "SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, mSPRT, doubly robust estimation" — matching the anchor for listing multiple specific concrete actions; it is comprehensive, not just a named domain.

It explicitly answers both what ("Running experiments out of the data warehouse...") and when ("Triggers on..." and "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"), so the explicit-trigger threshold for a 3 is met rather than capped at 2.

The "Triggers on..." clause covers natural phrasings a data scientist would say — "run experiments in BigQuery," "run experiments in Snowflake," "dbt experiments," "SQL t-test," "CUPED variance reduction," "sample ratio mismatch," "sequential testing" — giving good coverage of natural terms rather than only jargon.

The core niche (warehouse-native execution via SQL/dbt/CUPED) is distinct, but the trigger "choosing between platform and warehouse" / "build vs buy experimentation" overlaps with the sibling experimentation-platform-orchestrator skill that the body itself says owns that decision, so it could still trigger for the wrong skill; this is 'somewhat specific but could overlap' rather than 'unlikely to conflict'.