AB distributor-product-selection
Cross-platform product selection and sourcing analysis for distributors and dropshippers. Uses opencli plugins (aliexpress, alibaba-api buyer API, amazon) to cross-analyze supply channels (Alibaba distribution + AliExpress dropshipping) against retail channels (Amazon) and generate structured product research reports with margin calculations. Use this skill whenever the user mentions product selection, product research, finding products to sell, sourcing analysis, cross-border e-commerce sourcing, dropshipping product research, "what should I sell", "find winning products", comparing Alibaba vs AliExpress pricing, analyzing Amazon competition for sourcing decisions, or any task involving evaluating products across supply and retail platforms.
As a process B 78/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting
How to improve
- 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-keyunknown frontmatter key "requires" - note
frontmatter-keyunknown frontmatter key "primaryEnv" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 78/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 6 mutating operations with no state check
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3787 tokens
- low The response is described with custom markup (5 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
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 752: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (17 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.