Conciseness

The body is dense with non-generic operational knowledge (INTERNAL-scope key rejection, TTL-jitter partition spread, stale_failed/stuck_pending semantics, skip-reason interpretation) and contains no padding or explanations of concepts Claude already knows; it even adds token-efficiency guidance ('save to a file and project fields with jq; don't stream the raw body into the transcript'). Every token earns its place for this domain.

Actionability

Fully executable throughout: a copy-paste browser-devtools snippet for key creation, export commands, a both-region curl loop with an env-var guard, concrete jq projections, complete parameter tables, and 403 troubleshooting. The examples cover the common investigation cases directly.

Workflow Clarity

The 5-step Investigation workflow has an explicit healthy-baseline checklist ('failed reads a small fraction of total... stale_failed/stuck_pending at zero'), a conditional branch ('anything off → slowest_queries with a targeted filter'), an escalation path to the Metabase skill, and a reproducibility citation requirement. It is a read-only diagnostic workflow, so the destructive-operation cap does not apply.

Progressive Disclosure

Sections are well organized with clear headers and the cross-references that do exist (Metabase skill, backend/frontend source files) are clearly signaled with no nesting. However, the full endpoint and field-semantics API reference (~120 lines) is inlined with no bundle files at all, where a one-level-deep reference file could slim SKILL.md — a minor organization gap rather than the ideal split.

Specificity

Names multiple concrete actions with comprehensive coverage: 'Pull and interpret production experiment query-performance data', 'slowest experiment queries, precompute read/build health, and preaggregation cache footprint', plus 'all query params, and response field semantics (exception codes, exposure paths, precompute skip reasons, job states)'. No coverage gaps of the 4 anchor remain.

Completeness

Explicitly answers both questions: a clear 'what' (pull and interpret query-performance data, three analysis areas, params, field semantics, both regions, the read scope) followed by an explicit 'Use when...' clause with concrete trigger phrases — a direct match to the 5 anchor.

Trigger Term Quality

Natural trigger phrases a user would actually say: 'slow or failing experiment queries', 'precompute regressions', '307/159/241 errors', 'preaggregation table growth', 'how experiment query performance or the precompute rollout is doing in production' — including raw error codes users would quote verbatim.

Distinctiveness Conflict Risk

A clear niche with distinct triggers: staff-only /api/debug_ch_queries endpoints, the query_performance:read scope, prod-US/EU, and precompute-specific vocabulary. Minimal overlap risk with any other skill.