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Huawei Cloud CCE autoscaling failure diagnosis skill using Python SDK dispatcher. Use this skill when the user wants to: (1) diagnose CCE autoscaling failures across HPA not increasing Pod replicas, CCE elastic engine or Cluster Autoscaler not adding/removing nodes, missing metrics, missing CPU/memory requests, maxReplicas or max_nodes limits, Pending Pods, scheduling constraints, subnet IP exhaustion, ECS quota, or IAM agency permission issues, (2) perform HPA-to-CA cascade diagnosis linking workload-level and node-level scaling failures, (3) analyze CA Pod logs for Cluster Autoscaler signals (NoExpansionOptions, MaxNodeGroupSizeReached, QuotaExceeded, SubnetIPExhausted, IAM denied), (4) generate a complete Markdown diagnosis report with process, evidence, conclusion, confidence, and recommendations. Trigger: user mentions "autoscaling diagnosis", "弹性伸缩诊断", "HPA diagnosis", "HPA 诊断", "scaling failure", "伸缩失败", "HPA not scaling", "HPA 不伸缩", "replica scaling", "副本伸缩", "autoscaling issue", "伸缩问题"

ClawHub Agent Skills author: shijingcheng v0.1.3 MIT-0 53 files · 44 scripts body ≈ 4 492 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Huawei Cloud CCE autoscaling failure diagnosis skill using Python SDK dispatcher.

As a process C 66/100 · Has gaps — References files that are not bundled: references/verification-method.md

IntegrationKubernetesSoftware developmentData and analyticsSales and CRMtype 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
C
89/100
safety, quality, tests
Safety 60%
93
Quality 40%
83
Run on models
none yet
Process rating
C
66/100
Has gaps
References files that are not bundled: references/verification-method.md
Tools and files w 18
0
Failures and branches w 10
50
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • 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")

Files scanned: 53. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/verification-method.md
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 66/100

Will not run. References files that are not bundled: references/verification-method.md
  • 0Tools and files. 1 referenced file(s) missing: references/verification-method.md
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4492 tokens
  • 100Steps. 53 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +3Description length 1009: 120–800 characters recommended
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 53 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

External checks

ClawHub: suspicious
The skill is advertised as read-only autoscaling diagnosis, but its bundled dispatcher exposes broader cloud administration, credential-return, secret-reading, and state-changing actions.
LLM: suspicious (high) · VirusTotal: · 16 Jun 2026