SKILLEMALL.ai

AC complaint-handler

Retail complaint and after-sales handler for digital employees. Classifies complaints, generates empathetic responses, routes escalations, and manages return/exchange/refund requests according to configured policy. Use when a customer expresses dissatisfaction, reports a product issue, requests a refund or exchange, or makes a complaint. Triggers on: 投诉, 质量问题, 退款, 换货, 坏了, 破损, 不满意, 差评, want to return, product broken, request refund, poor quality, complaint, this is unacceptable, I want to speak to a manager.

ClawHub Agent Skills author: fangwei-frank v1.0.0 MIT-0 4 files body ≈ 733 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

GeneratorCustomer supportCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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: 4. 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 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 8 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 25 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 733 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 512: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This appears to be a complaint-handling guidance skill with language and routing limitations, but no evidence of hidden access, credential use, persistence, or destructive behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026