Conciseness

Mostly efficient — dense PromQL specifics, no beginner-concept padding — but the 12-entry Anti-Patterns section (~90 lines) duplicates material twice over: entries like 'NEVER use a regex matcher when the label value is known', 'NEVER omit the for clause', and 'NEVER express an error rate as a raw count' restate the body's own Key Rules 1–5, while rate()-on-gauge, averaging-quantiles, and high-cardinality entries already exist in references/best_practices.md's 'Common Anti-Patterns' section. Not 4 because this goes beyond minor trimming, and not 2 because nothing is padded fluff — every entry is domain-specific.

Actionability

Fully executable throughout: copy-paste-ready PromQL patterns (request rate, error ratio, P95 for classic and native histograms, multi-window burn rate), concrete commands ('promtool check rules assets/alerting_rules.yaml', the curl query example, 'grep -n "^# " assets/common_queries.promql'), a YAML alerting/recording snippet, and a scenario→reference routing table. Not 4 because common cases are covered with no gaps — the examples are complete queries, not pseudocode.

Workflow Clarity

The 7-stage workflow is clearly sequenced with an explicit validation feedback loop at stage 6 ('Fix any issues and re-validate until all checks pass'), a structured Validation Checklist output format, a follow-up 'Verifying Generated Rules' section with promtool commands, and an 'Ask vs. Infer' note plus stage-3 pre-filled defaults that prevent needless back-and-forth. This matches the 5-anchor validate→fix→retry pattern.

Progressive Disclosure

Good structure: a scenario→reference routing table, a References section annotating all 11 bundle files, all referenced paths verified to exist, and references confirmed one level deep (intra-file TOCs plus one sibling cross-link, no nesting). Not 5 because the large inline Anti-Patterns block is content that largely belongs in references/best_practices.md, which already covers the same anti-patterns — the body should point there rather than re-inline it.

Specificity

The description lists five concrete, domain-qualified actions — 'calculating error rates, aggregating metrics across labels, creating histogram percentiles, writing recording rules, and building SLO burn-rate alerts' — which comprehensively covers the query-generation niche. Not 4 because there are no meaningful coverage gaps within the stated scope (validation is deliberately excluded as a separate skill's job).

Completeness

Explicitly answers both questions with concrete trigger phrases: the first sentence states exactly what the skill does, and 'Use when creating new PromQL queries, implementing monitoring and alerting rules, building observability dashboards...' states when with five distinct triggers. This mirrors the rubric's 5-anchor example structure.

Trigger Term Quality

Strong natural triggers users would actually say: 'PromQL queries', 'error rates', 'recording rules', 'SLO burn-rate alerts', 'monitoring and alerting rules', 'observability dashboards', 'counters, gauges, histograms, summaries', 'RED... and USE... monitoring patterns'. Not 5 because a few common phrasings are absent — 'latency'/'availability' queries, file extensions like .promql/.yaml, and 'promtool' are missing.

Distinctiveness Conflict Risk

The niche is clear — PromQL generation for Prometheus — with distinct triggers that separate it from the sibling validator skill, and the body's 'When Not to Use' section reinforces the boundary. Not 5 because 'building observability dashboards' is a broad phrase that could overlap with a general dashboarding skill.