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AB huawei-cloud-cce-capacity-trend-forecaster

Use when analyzing Huawei Cloud CCE periodic capacity trends, forecasting resource bottlenecks, simulating node/workload elasticity policies, generating capacity curve charts and reports, comparing recurring history records, or tuning HPA and node autoscaler configurations. Trigger: user mentions "capacity forecast", "容量预测", "capacity trend", "容量趋势", "resource trend", "资源趋势", "capacity planning", "容量规划", "capacity risk", "容量风险", "resource exhaustion", "资源耗尽", "HPA tuning", "node autoscaler", "capacity report", "capacity simulation"

ClawHub Agent Skills author: shijingcheng v0.1.2 MIT-0 52 files body ≈ 5 308 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Use when analyzing Huawei Cloud CCE periodic capacity trends, forecasting resource bottlenecks, simulating node/workload elasticity policies, generating…

As a process B 71/100 · Nearly there — weak spots: running it twice

AnalyzerKubernetesData 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
71/100
Nearly there
Running it twice w 4
30
When it triggers w 12
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 25. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5308 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 71/100

  • 30Running it twice. 14 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
  • 70Execution cost. Instruction body is 5308 tokens
  • 85Steps. 48 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 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 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)
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 10 example trigger phrases
  • +3Description length 537: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 48 items
  • +3Output format is stated explicitly
  • +4Has examples (8 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 advertised as a CCE capacity forecaster, but its shipped dispatcher exposes much broader cloud and Kubernetes control actions, credential-bearing outputs, and sensitive data access than the description discloses.
LLM: suspicious (high) · VirusTotal: · 16 Jun 2026