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ops-inspector

AIOps-style CloudBase inspection skill (v3). Use when users need 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. Triggers on 巡检, 诊断, 告警, 峰值 QPS, 限频, 调用量为 0, troubleshooting.

72

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured operational skill body: fully concrete tool calls, clearly sequenced workflows with validation checkpoints, and genuine progressive disclosure into two real reference files. The main weakness is token efficiency — the same critical rules (callCloudApi prohibition, alarm reference pointer) are restated several times across sections.

Suggestions

State the "never use callCloudApi for metrics; always queryEnv(action=\"metrics\")" rule once in a prominent location and reference it from the other sections instead of repeating it in "Do NOT use for", "Common mistakes", and "AIOps Methodology".

Trim the overlap between the "Activation Contract" section and the "Inspection Modes" table — the mode table alone could carry the when-to-use-what mapping, shortening the front of the skill.

Point to references/alarm-interpretation.md once in the "Then also read" section and once at the point of use in the alarm-interpretation mode, rather than repeating the pointer in the checklist, workflow steps, and targeted workflow.

DimensionReasoningScore

Conciseness

The body is mostly efficient — dense tables, exact tool calls, no explanation of concepts Claude already knows — but it could be tightened: the "never use callCloudApi for metrics" rule is restated at least three times ("Do NOT use for", "Common mistakes", "AIOps Methodology"), the alarm-interpretation reference is pointed to repeatedly, and the "Activation Contract" section partially duplicates the later "Inspection Modes" table. This matches the anchor for mostly efficient with some unnecessary repetition, rather than the 4 anchor's minor trims.

3 / 5

Actionability

Guidance is fully executable throughout: exact MCP invocations with parameters ("queryEnv(action=\"metrics\", envId=\"<EnvId>\", metricName=\"GatewayTraceEnvQPS\")"), a copy-paste report template, a Preferred Tool Map, concrete CLS queryString patterns per scenario, and an explicit timestamp format. The only placeholders are "<name>"-style values that depend on user state, and those are specified elsewhere. Not a 4 because specific examples cover the common cases end to end.

5 / 5

Workflow Clarity

The Full Inspection Workflow is a clearly sequenced 7-step process with explicit validation checkpoints: environment binding checked before anything else (Step 1), CLS service status verified before log searches (Step 3), a "jump to the matching playbook" feedback path on error patterns (Step 6), and a closing Minimal Checklist. All operations are read-only, so the destructive/batch validation cap does not apply. Not a 4 because checkpoints are explicit rather than implicit at every risky transition.

5 / 5

Progressive Disclosure

The body is an overview that delegates depth to real, well-signaled, one-level-deep references — "Alarm interpretation baselines -> references/alarm-interpretation.md" and "Fault playbooks ... -> references/fault-playbooks.md" both exist as files, are named in a dedicated "Then also read" section, and are re-pointed to at the point of need. Sibling skill pointers are clearly labeled with a documented fallback for when they are absent. Not a 4 because navigation is easy and content is appropriately split with no burial or nesting.

5 / 5

Total

18

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete capability list, explicit bilingual trigger guidance, and a clear read-only diagnostic niche. The only flaw is the mild "AIOps-style (v3)" buzzword/version prefix, which adds noise without adding capability information.

DimensionReasoningScore

Specificity

The description lists several concrete actions — "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" — matching the anchor for multiple specific actions with only minor gaps. It is not a 5 because the "AIOps-style ... (v3)" prefix is buzzword/version noise rather than a capability, and the actions stop just short of comprehensive coverage of the skill's own modes (e.g., error log aggregation and report generation are only implied).

4 / 5

Completeness

It explicitly answers both questions: what ("health checks, log diagnosis, alarm interpretation ... metrics via queryEnv(action=metrics) ... fault playbooks for 429 / function 404 / ACCESS_TOKEN_INVALID / zero invocations") and when ("Use when users need ..." plus a dedicated "Triggers on ..." clause with concrete trigger phrases). This matches the anchor for clearly and explicitly answering both what AND when with concrete trigger phrases, exceeding the 4 anchor whose 'when' is less specific.

5 / 5

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 phrases in the body of the description ("health checks", "CPU alert normal?", "peak QPS", "429", "zero invocations") gives comprehensive bilingual synonym coverage of exactly what affected users would say. Not a 4 because there are no natural terms materially missing — both Chinese and English phrasings of every trigger scenario are present.

5 / 5

Distinctiveness Conflict Risk

The niche is clearly bounded to read-only CloudBase diagnostics with distinct triggers (specific error codes like ACCESS_TOKEN_INVALID, Chinese ops terms like 调用量为 0, and metric questions), which sibling implementation skills (cloud functions, CloudRun, databases, auth) would not naturally trigger on. Not a 4 because overlap risk with closely related skills is minimal — the diagnostic/inspection framing and error-code-specific triggers separate it cleanly.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
TencentCloudBase/CloudBase-AI-Toolkit
Reviewed

Table of Contents

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