SKILLEMALL.ai

AB huawei-cloud-vod-collector

Invoke this skill to capture poor experiences and distill them into high-value requirements (Voice of Developer). Use when user encounters any Huawei Cloud related issues, like user expresses dissatisfaction, encounters errors, or wants to report issues/suggestions.Triggers include: "体验差","反馈问题","反馈建议","这个有bug","拒绝了请求","报告问题","反馈体验","report a problem","report a suggestion","bug report","poor experience","voice of developer"

ClawHub Agent Skills author: dev-skills-operator v1.0.3 MIT-0 21 files · 8 scripts body ≈ 2 805 tokens Open the sourceclawhub.ai↗ analyzed 11 d ago

Invoke this skill to capture poor experiences and distill them into high-value requirements (Voice of Developer).

As a process B 65/100 · Nearly there — weak spots: result and completion, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
65/100
Nearly there
Failures and branches w 10
0
Result and completion w 14
40
When it triggers w 12
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: huawei-cloud-vod-collector (ClawHub)

How to improve

    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: 21. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Failures and branches. Linear process with no failure handling
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 56 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2805 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • high The skill tells the model to perform an irreversible action with no human approval
    • low The response is described with custom markup (14 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)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • -32 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 427: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 56 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This feedback skill is visible about its goals, but it also installs persistent tools, stores a login token, and can submit captured issue context, so it needs review before use.
    LLM: suspicious (high) · 20 Sept 2026