Conciseness

The body is dense and table-driven, but the 'AIOps Methodology' section ('Data Collection', 'Pattern Recognition', 'Root Cause Hypothesis') is conceptual padding, routing info is repeated across the 'Sibling skills' preamble and 'Then also read'/'Related Skills' sections, and `references/alarm-interpretation.md` is pointed to 3+ times. Anchor 4 fits: efficient with minor instances of over-explanation that could be trimmed.

Actionability

Fully executable guidance throughout: exact MCP calls with actions and params (`queryEnv(action="metrics", envId="<EnvId>", metricName="GatewayTraceEnvQPS")`), a Preferred Tool Map, a CLS query-pattern table, concrete time formats (`"2026-08-17 00:00:00"`), and a copy-paste report template covering the common cases. Not 4 — there are no meaningful gaps in concreteness.

Workflow Clarity

The 7-step Full Inspection Workflow is explicitly sequenced with validation checkpoints (Step 1 environment binding, Step 3 CLS log-service status with a graceful degradation path), error-recovery branches ('Timeout / 429 / 404 / ACCESS_TOKEN_INVALID patterns → jump to the matching playbook'), and a Minimal Checklist. The skill is read-only diagnostics, so the destructive-operation cap does not apply; this matches anchor 5 rather than 4 because validation and feedback loops are explicit, not implicit.

Progressive Disclosure

Both referenced files exist, are one level deep, and are clearly signaled with purpose ('Alarm interpretation baselines -> references/alarm-interpretation.md', 'Fault playbooks (429 / 404 / token / zero calls) -> references/fault-playbooks.md'), and the bulk detail (baselines, playbooks) is correctly split out. Held at 4 rather than 5 because the ~285-line body inlines reference-scale material (the full report template, the tool map, the CLS query table) that could live in a reference file.

Specificity

The description lists multiple specific concrete actions with comprehensive coverage: 'health checks, log diagnosis, alarm interpretation (CPU alert normal?, peak QPS), metrics via queryEnv(action=metrics), or fault playbooks for 429 / function 404 / ACCESS_TOKEN_INVALID / zero invocations'. Only 'AIOps-style' is mild buzzword padding, which is not enough to drop to anchor 4 ('minor gaps in coverage').

Completeness

It explicitly answers both questions: what ('AIOps-style CloudBase inspection skill (v3)' with its enumerated capabilities) and when ('Use when users need health checks... or fault playbooks for...' plus a dedicated trigger list), matching the anchor-5 good example's structure exactly.

Trigger Term Quality

'Triggers on 巡检, 诊断, 告警, 峰值 QPS, 限频, 调用量为 0, troubleshooting' plus the English symptom terms in the 'Use when' clause gives comprehensive natural coverage including bilingual synonyms and specific error codes. Not 4 — no common variation a user would plausibly say is missing.

Distinctiveness Conflict Risk

The read-only inspection/diagnosis niche is clear with highly distinct triggers (specific error codes, Chinese ops terms like 巡检/峰值 QPS), but generic terms such as 'troubleshooting' and '诊断' create minor overlap risk with the closely related implementation skills it routes to (cloud-functions debugging, cloudrun-development). Anchor 4 ('minor overlap risk with closely related skills') fits better than 5.