AF walmart-keyword-search
Walmart keyword search scraper: input a search keyword and page number, navigate to walmart.com search results, extract paginated product listings with itemId, url, title, brand, image, price, wasPrice, rating, reviewCount, availability, seller info, fulfillmentBadge, classType, and shortDescription. Use when user mentions walmart search, walmart keyword search, search walmart products, scrape walmart search results, walmart search scraper, walmart product search, search items on walmart, walmart search by keyword, walmart product listing, get walmart search data, extract walmart products, walmart search results scraper, walmart shop search, walmart catalog search, walmart product list by keyword, walmart browse by keyword. Also applies to price comparison research on walmart, finding walmart product URLs in bulk, monitoring walmart search rankings, collecting walmart product data by category keyword.
Walmart keyword search scraper: input a search keyword and page number, navigate to walmart.com search results, extract paginated product listings with…
As a process F 54/100 · Will not run — References files that are not bundled: scripts/*.py
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/*.py
Process rating: all ten parameters 54/100
- 0Tools and files. 1 referenced file(s) missing: scripts/*.py
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 16 steps, 2 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1535 tokens
- low 11 top-level sections: this looks like several domains in one skill
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)
- +3Description length 914: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 14 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.