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BC huawei-cloud-cce-dependency-impact-analyzer

Huawei Cloud CCE service topology dependency impact analysis skill that traces Service/Ingress/Pod/Node propagation paths and upstream/downstream blast radius. Use this skill when a CCE incident needs service topology impact analysis, including Service/Ingress/Pod/Node propagation paths, upstream/downstream blast radius, affected entrypoints, and a complete Markdown impact report with evidence and confidence limits. Trigger: "dependency impact", "依赖影响", "dependency analysis", "依赖分析", "cascade failure", "级联故障", "blast radius", "爆炸半径", "service topology", "服务拓扑", "propagation path", "传播路径", "upstream downstream impact", "上下游影响", "dependency mapping", "依赖映射"

ClawHub Agent Skills author: shijingcheng v0.1.1 MIT-0 52 files · 44 scripts body ≈ 3 367 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

Huawei Cloud CCE service topology dependency impact analysis skill that traces Service/Ingress/Pod/Node propagation paths and upstream/downstream blast radius.

As a process C 63/100 · Has gaps — weak spots: failures and branches, running it twice, progress reporting

AnalyzerKubernetesInfrastructureData 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
B
92/100
safety, quality, tests
Safety 60%
92
Quality 40%
92
Run on models
none yet
Process rating
C
63/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 8

    ✓ No critical or high findings

    Medium and low: 8
    • 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(
    • low Secrets in code secret-password-literal scripts/huawei_cloud/cce_node.py:646
      Hard-coded password / key literal (may be an example)
      login = Login(user_password=UserPassword(username="root", password=salted_b64))
    • low Secrets in code secret-password-literal scripts/huawei_cloud/cce_nodepool.py:456
      Hard-coded password / key literal (may be an example)
      user_password.password = base…ode(hashed.encode("utf-8")).decode("utf-8")
    • low Dangerous commands cmd-execpolicy-bypass SKILL.md:48
      Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)
      - Windows: `skill action=exec: powershell -ExecutionPolicy Bypass -File skill://scripts/chec…ps1`
      quoted

    Files scanned: 52. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "id"

    Process rating: all ten parameters 63/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 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
    • 100Steps. 62 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3367 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill

    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 8 example trigger phrases
    • +3Description length 663: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 62 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    The skill presents itself as read-only dependency analysis, but the bundled dispatcher can read sensitive cluster data and perform live cloud or Kubernetes changes.
    LLM: suspicious (high) · VirusTotal: · 16 Jun 2026