CB 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".
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
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5307 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "triggers" - note
edit-residuethe text marks something as outdated (lines 119, 126): check that old rules are not kept next to new ones — the full check reads the text for contradictions
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.