SKILLEMALL.ai

AB return-rate-reducer

Reduce e-commerce return rates through data-driven root-cause analysis, product-page fixes, and policy optimization. Use this skill whenever the user mentions return rate, refund rate, high returns, return reasons, size-related returns, expectation mismatch, product-description accuracy, return policy, return abuse, reverse logistics cost, reducing returns, return prevention, or wants to analyze why customers return products. Also trigger when the user shares return data, reviews mentioning disappointment or "not as described," or asks how to improve product pages to prevent returns — even if they don't explicitly say "return rate." Covers any e-commerce category (fashion, electronics, beauty, home, pet, food, etc.).

ClawHub Agent Skills author: RIJOY-AI v0.1.0 MIT-0 13 files body ≈ 2 176 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerLogistics and warehouseInfrastructureCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 10. 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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 41 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2176 tokens
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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)
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 726: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 41 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: clean
    This skill provides return-rate analysis guidance and optional local CSV/PDP analysis scripts without evidence of hidden access, persistence, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026