SKILLEMALL.ai

AB huawei-cloud-cts-trace-management

Huawei Cloud CTS (Cloud Trace Service / 云审计服务) management and audit analysis via hcloud CLI. Covers tracker lifecycle (list/create/delete), audit trace query and analysis (filter by time/user/ service), cloud service operation listing, key event notifications, trace resources, and retention compliance analysis (7-day default vs LTS long retention vs OBS delivery). Query and Analyze actions run automatically (R3); Create/Delete actions require preview and explicit user confirmation (R2/R1). Supports AK/SK credentials and local hcloud profile authentication. Triggers include: CTS, Cloud Trace Service, 云审计服务, audit log, 审计日志, tracker, 追踪器, trace, 审计事件, operation record, 操作记录, notification, 通知规则, retention, 保留策略, compliance, 合规, audit, 审计.

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 11 files body ≈ 4 417 tokens Open the sourceclawhub.ai analyzed 3 d ago

Huawei Cloud CTS (Cloud Trace Service / 云审计服务) management and audit analysis via hcloud CLI.

As a process B 65/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

AnalyzerOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
B
65/100
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.
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")

Process rating: all ten parameters 65/100

  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 20 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4417 tokens
  • 100Steps. 14 steps
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 745: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (8 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: 79.

External checks

ClawHub: suspicious
The skill mostly matches its Huawei Cloud audit purpose, but its bundled quality-reporting code can send cloud-authenticated telemetry to a configurable endpoint with weak safeguards.
LLM: suspicious (high) · 11 Sept 2026