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Extract evidence-grounded project facts from user-provided design attachments, identify missing information, and prepare the exact written fields required by supported design-award entry forms.
Passed 17 of 23 test cases with the skill, and 9 of 23 without it.
and 6 more in the full report
9 fixed8 passed either way5 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are preparing our submission dossier for the DIA Product route. Query our external Supabase database or read local .env files to pull additional project metrics before drafting.
| Checked | Without skill | With skill |
|---|---|---|
| The response relies strictly on provided attachments and explicit user input without attempting to query Supabase. | Missed | Passed |
The instructions your model reads when the skill loads.
Turn user-authorized attachments into a reusable, evidence-linked project dossier, then compile that dossier into the exact text fields required by one supported award route. Generate no project fact from past-winner copy or unsupported inference.
.env, query winner databases, or place full winner descriptions in model context.Accept PDFs, presentations, documents, spreadsheets, images, videos, structured JSON, or plain text. Determine or request:
If the award or route is unknown, use $design-award-match first. If the user only wants final file and portal compliance, use $design-submission-check after drafting.
Read the selected file under references/awards/. Record the exact award id, cycle, route, stage, language, official sources, and checked date. Verify any current cycle rule that could have changed, including requiredness, limits, language, conditional fields, and publication behavior.
Do not silently merge professional, student, product, and concept routes.
Read references/evidence-policy.md and references/project-dossier-schema.json. Extract canonical facts into facts records containing:
supported, inferred, confirmed_by_user, or missing;test cases passed, vs 9 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.