SKILLEMALL.ai

AB necessity-pain-point-selection

Helps merchants selling utility / problem-solution products (car storage, multi-use kitchen shears, storage boxes, cleaning tools, etc.) do assortment and product improvement via VOC-based selection (voice of customer from reviews). Trigger when users mention review analysis, negative-review pain points, user complaints, selection from reviews, basis for feature improvements, competitor negative reviews, real buyer needs, "our bad reviews keep mentioning X," "which subcategory should I pick," or reducing returns by fixing product issues—even if they do not say "pain point" or "VOC" explicitly.

ClawHub Agent Skills author: RIJOY-AI v0.1.2 MIT-0 10 files body ≈ 2 834 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process B 76/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerCustomer supportInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
99
Run on models
none yet
Process rating
B
76/100
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: 8. 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 76/100

    • 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
    • 70Failures and branches. 4 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 65 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2834 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 600: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 65 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 3)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.

    External checks

    ClawHub: clean
    This skill is a focused product-review analysis aid with an optional local script for classifying user-provided reviews.
    LLM: benign (high) · VirusTotal: · 29 May 2026