SKILLEMALL.ai

AB product-spy

Meta-skill for finding e-commerce winning products by correlating social hype signals with marketplace competition data and preparing deployment-ready store listings. Use when users want trend scouting for dropshipping/white-label opportunities with explicit data gates and execution handoff.

modbender/skill-library-mcp Claude Code author: modbender MIT 2 files body ≈ 1 746 tokens Open the sourcegithub.com analyzed 2 d ago

Meta-skill for finding e-commerce winning products by correlating social hype signals with marketplace competition data and preparing deployment-ready store…

As a process B 72/100 · Nearly there — weak spots: when it triggers, consistency, running it twice

IntegrationShopifyWordPressInfrastructureCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
72/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 72/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 11 mutating operations with no state check
    • 40Consistency. Frontmatter name (product-spy) differs from the folder (product-research)
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 109 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 1746 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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 292: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 109 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.