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AB huawei-cloud-cce-observability-context-builder

Use this skill when the user wants to collect AOM alarms, metrics, LTS logs, Pod logs, or Kubernetes events and build a comprehensive observability context package before handing off to diagnosis skills. Trigger: user mentions observability context, "可观测性上下文", context builder, "上下文构建", metric+log+event, "指标+日志+事件", comprehensive observability, "综合可观测", diagnosis context, "诊断上下文

ClawHub Agent Skills author: shijingcheng v0.1.1 MIT-0 52 files body ≈ 4 376 tokens Open the sourceclawhub.ai↗ analyzed 34 h ago

Use this skill when the user wants to collect AOM alarms, metrics, LTS logs, Pod logs, or Kubernetes events and build a comprehensive observability context…

As a process B 72/100 · Nearly there — weak spots: running it twice

ProcedureKubernetesInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
B
72/100
Nearly there
Running it twice w 4
30
Failures and branches w 10
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:665
      High-entropy token-like string (may be an id, hash or a credential)
      AddO…ody,
    • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:737
      High-entropy token-like string (may be an id, hash or a credential)
      body = AddO…ody(
    • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:932
      High-entropy token-like string (may be an id, hash or a credential)
      Dele…ody,
    • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:1336
      High-entropy token-like string (may be an id, hash or a credential)
      AddO…ody,
    • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:1372
      High-entropy token-like string (may be an id, hash or a credential)
      body = AddO…ody(

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 3, column 14: description: Use this skill when the user wants to collect AOM alarms, metrics,… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
    • note frontmatter-key unknown frontmatter key "id"

    Process rating: all ten parameters 72/100

    • 30Running it twice. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4376 tokens
    • 85Steps. 76 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 380: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 76 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    The skill is presented as read-only observability, but the package exposes broad cloud and Kubernetes admin actions plus sensitive credential-handling paths.
    LLM: suspicious (high) · 16 Jun 2026