SKILLEMALL.ai

BB huawei-cloud-deployment-task-management

Huawei Cloud CloudDeploy (CodeArts Deploy / 部署) management, execution, and failure analysis using the KooCLI hcloud command-line client. Covers deploy application listing and creation, deployment task listing/detail/creation, task start, task deletion, deployment failure root-cause analysis (agent offline, timeout, missing artifact, permission), and OBS artifact link verification. Query and Analyze actions run automatically (R3); Create/Start actions require preview and user confirmation (R2); Delete requires explicit confirmation (R1). Supports AK/SK credentials and local hcloud profile authentication. Triggers include: "CloudDeploy", "CodeArts Deploy", "部署", "deploy task", "deployment", "deployment task", "deploy application", "部署任务", "部署应用", "start deploy", "发布", "release", "artifact deployment", "制品部署", "deploy failure", "部署失败", "pipeline deployment", "CI/CD deployment".

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 11 files body ≈ 5 307 tokens Open the sourceclawhub.ai analyzed 3 d ago

Huawei Cloud CloudDeploy (CodeArts Deploy / 部署) management, execution, and failure analysis using the KooCLI hcloud command-line client.

As a process B 70/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

ProcedureMySQLInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
B
70/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 · 0

✓ No critical or high findings

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5307 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 70/100

  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 92 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5307 tokens
  • 100Steps. 23 steps
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 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
  • high The skill tells the model to perform an irreversible action with no human approval
  • low The response is described with custom markup (7 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)
  • +3Description length 887: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 12 example trigger phrases
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (5 of 6)
  • +3All 1 scripts are documented

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

External checks

ClawHub: suspicious
This CloudDeploy skill mostly matches its stated purpose, but it needs review because it combines broad cloud deployment authority with default outbound quality reporting that can send runtime data and an IAM token.
LLM: suspicious (high) · 11 Sept 2026