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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 / AI model 429 / function timeout / 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.

Quality

93%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is ops-inspector in TencentCloudBase/CloudBase-AI-Toolkit

SKILL.md
Quality
Evals
Security

Quality

Content

88%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-built operational skill body: exact tool calls, sequenced workflows with precondition checks, a symptom-to-queryString mapping table, and a report template make it highly actionable with clear disclosure into real reference files. The main improvement areas are trimming the six-fold repetition of the playbook symptom list and tightening cross-skill reference path consistency.

Suggestions

State the playbook symptom list (429 / AI model 429 / function timeout / function 404 / ACCESS_TOKEN_INVALID / 调用量为 0) once in a canonical location such as the Inspection Modes table, and refer back to it elsewhere ('the six playbook symptoms, see above') instead of repeating the full enumeration six times.

Use the ../ prefix consistently for cross-skill references — the Targeted Inspection section cites 'cloudrun-development/references/vpc-and-database.md' without it, unlike the sibling skill references used elsewhere.

Consolidate the three separate statements of the 'never use callCloudApi for metrics' rule (Do NOT use for, Common mistakes, AIOps Methodology) into one prominently placed rule and drop the duplicates.

DimensionReasoningScore

Conciseness

The body is dense with operational content and avoids explaining concepts Claude already knows, but the playbook symptom list ('429 / AI model 429 / function timeout / function 404 / ACCESS_TOKEN_INVALID / 调用量为 0') is repeated nearly verbatim six times, and the callCloudApi prohibition appears three times. It sits between anchors 3 and 4: efficient overall with systematic repetition that could be trimmed, but the repetition is small in proportion and nearly everything else earns its tokens.

4 / 5

Actionability

Gives exact executable MCP calls throughout — `queryEnv(action="metrics", envId, metricName="GatewayTraceEnvQPS")`, a full Preferred Tool Map, a Common CLS Query Patterns table with literal queryString values, and a copy-paste report template with console URLs. Specific examples cover all common cases across the four inspection modes, matching the anchor-5 'fully executable; specific examples cover the common cases'.

5 / 5

Workflow Clarity

The Full Inspection Workflow sequences seven numbered steps with explicit validation checkpoints — environment binding before anything else, `queryLogs(action="checkLogService")` before log diagnosis, 'Read references/alarm-interpretation.md before concluding' — plus a Minimal checklist and conditional error routing ('a 429 from an AI model call goes to Playbook 5, not Playbook 1'). Not 4 because checkpoints, checklist, and recovery routing are all explicit; the skill is read-only, so the destructive/batch validation cap does not apply.

5 / 5

Progressive Disclosure

The ~200-line fault playbooks and alarm baselines are correctly split into real one-level-deep references (references/fault-playbooks.md and references/alarm-interpretation.md, both present in the bundle), clearly signaled in 'Then also read' and in-line. Not 5 because many referenced paths point outside the bundle to sibling skills that do not exist in this environment, and one reference ('cloudrun-development/references/vpc-and-database.md') is written without the ../ prefix used elsewhere — minor organization gaps per anchor 4.

4 / 5

Total

18

/

20

Passed

Description

96%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.

An unusually strong description: it states what the skill does via concrete capabilities and specific error codes, and provides explicit when-to-use triggers in both English and Chinese. The only weakness is minor overlap risk with closely related sibling CloudBase skills on broad terms like 'troubleshooting'.

DimensionReasoningScore

Specificity

Names multiple concrete capabilities — 'health checks, log diagnosis, alarm interpretation (CPU alert normal?, peak QPS), metrics via queryEnv(action=metrics)' — plus fault playbooks for specific error codes ('429 / AI model 429 / function timeout / function 404 / ACCESS_TOKEN_INVALID / zero invocations'). This matches the anchor-5 'multiple specific concrete actions; comprehensive coverage'; it is not a 4 because no capability area of the skill is left unnamed.

5 / 5

Completeness

Explicitly states what the skill is ('AIOps-style CloudBase inspection skill (v3)') with an enumerated capability list, and gives an explicit 'Use when users need...' clause plus a 'Triggers on...' list of concrete trigger phrases. Both what and when are explicit and concrete, matching the anchor-5 structure.

5 / 5

Trigger Term Quality

Includes natural phrases users would actually say in both English ('health checks', 'log diagnosis', 'CPU alert normal?', 'peak QPS', 'troubleshooting') and Chinese ('Triggers on 巡检, 诊断, 告警, 峰值 QPS, 限频, 调用量为 0'). The bilingual synonym coverage matches the top anchor's 'comprehensive coverage of natural terms including synonyms'; not a 4 because common variations are covered in two languages.

5 / 5

Distinctiveness Conflict Risk

Scoping to 'CloudBase' inspection with distinctive symptom triggers ('ACCESS_TOKEN_INVALID', '调用量为 0') gives a clear niche, but the broad terms 'troubleshooting' and 'health checks' plus sibling CloudBase skills covering related debugging territory create minor overlap risk. It sits between anchors 4 and 5: not 5 because a generic 'troubleshooting' or 'function timeout' request could reasonably route to a sibling implementation skill instead.

4 / 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

Path

config/.claude/skills/ops-inspector/SKILL.md

Reviewed

Table of Contents

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