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Find and verify award-winning designs in the same or adjacent functional category through eight explicit relevance dimensions: problem and user, core function, sensing technology, intervention mechanism, physical form, use context and workflow, system architecture, and visual language.
Passed 14 of 17 test cases with the skill, and 4 of 17 without it.
and 18 more in the full report
14 fixed4 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
When verifying image evidence for a visual-language search candidate, the visual evidence script successfully downloads the official image asset, but no visual inspection tool is available to inspect the image pixels. What exact status must be assigned?
| Checked | Without skill | With skill |
|---|---|---|
| Assign status Pending visual inspection - visual_tool_unavailable when visual inspection tools are absent | Missed | Passed |
| Refrain from marking accessibility failure as image inaccessible when the official image payload was fetched | Passed | Passed |
The instructions your model reads when the skill loads.
Retrieve a small, high-precision set of verified award-winning designs. Keep the functional design category as a mandatory boundary, then search separately across eight relevance dimensions. Search public official sources at request time. Do not connect to, package, or depend on a private award database.
Accept a project description, image set, PDF, project page, or brief. Accept optional dimensions, award sources, years, and result count.
Require enough information to identify the object or service and its primary function. If the primary function remains ambiguous, ask exactly one short question: What is the design's primary function and who uses it? Otherwise state reasonable assumptions and continue.
When the user does not select dimensions, use balanced mode across every dimension supported by the supplied evidence. Use user images to infer physical-form and visual-language terms. If no image is supplied, do not activate visual-language relevance unless the user supplies explicit visual descriptors.
Offer this template when the user asks how to use the skill:
Project: {name or object}
Primary function: {problem solved or job performed}
Target user: {optional}
Use context: {optional}
Relevance dimensions: {all or selected dimensions}
Preferences: {optional award sources, years, and result count}
Read ../design-judge-shared/category-taxonomy.md and references/relevance-dimensions.md. Extract:
test cases passed, vs 4 / 17 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.