Design Award Match

Design Award Match

Purpose

Identify the most defensible award, program, track, and category for a design project. Treat fit scores as transparent decision aids, never as probabilities of winning.

Scope

• Extract decision-relevant project facts.
• Pre-filter the configured award allowlist with stable eligibility gates.
• Verify all dynamic requirements on current official pages.
• Compare project evidence with published criteria and observable winner trends.
• Score, rank, and explain strategic fit.
• Stop after recommendation; do not audit every submission file or redesign the project.

Use $design-award-search for verified same-category winners. Route file format, size, naming, declarations, and upload completeness to $design-submission-check.

Input Contract

Accept a brief, images, PDF, portfolio page, or structured JSON. Extract or request only facts that can change the recommendation:

• primary function, target user, and use context;
• innovation and supporting evidence;
• project state and completion, launch, or release date;
• applicant type, student status, country or region;
• candidate awards, intended cycle, budget, and geographic constraints.

Use the canonical values in references/category-crosswalk.json. If the primary function is unclear, ask one short question. If an eligibility fact is missing, continue with Eligibility: Unknown; never assume a pass.

Offer this template when the user asks how to use the skill:

Project: {name and one-sentence description}
Primary function: {job performed or problem solved}
Target user / context: {optional}
Innovation and evidence: {optional}
Development status / launch date: {optional}
Applicant: {student, individual, studio, company; country/region}
Candidate awards: {optional; omit to search the supported allowlist}
Submission cycle / constraints: {optional year, region, budget}

Workflow

1. Build the project profile

Read ../design-judge-shared/category-taxonomy.md and references/category-crosswalk.json. Classify by primary function before appearance. Record one canonical category, no more than two adjacent categories, project state, applicant type, evidence, timing, and constraints. Label material inferences.

For command-line pre-filtering, prepare JSON like examples/project-profile.example.json.

2. Validate and load the supported award set

Read references/awards/index.json and references/award-profile-guide.md. Analyze only the programs in the allowlist. Treat Red Dot Product and Red Dot Design Concept as separate programs. If a requested award is absent, return Unsupported rather than researching and silently adding it.

When a shell is available, validate configuration before analysis:

python scripts/validate_award_profiles.py --pretty

Build a focused candidate set, normally three to five routes:

python scripts/build_candidate_set.py examples/project-profile.example.json --limit 5 --pretty

Use --award repeatedly to restrict candidates. Award ids and declared aliases are accepted.

3. Apply stable gates

Use each selected profile's routes, required_project_fields, and stable_constraints. When needed, run:

python scripts/filter_eligible_awards.py examples/project-profile.example.json --include-ineligible --pretty

Assign:

• Eligible: all stable and current official gates pass.
• Ineligible: a confirmed rule excludes the entry.
• Unknown: a project fact is missing or any live gate remains unchecked.

Exclude Ineligible routes from ranking but state the exact reason. Keep Unknown routes conditional.

4. Verify dynamic rules live

Read only the selected award profiles under references/awards/. For every dynamic_gate and relevant dynamic_field, verify current official pages at request time:

• cycle status and absolute deadlines;
• applicant, geography, age, enrollment, and graduation rules;
• completion, publication, distribution, or launch windows;
• exact track and category labels;
• current judging criteria;
• enough material and physical-delivery requirements to assess feasibility;
• fees and mandatory winner obligations when they affect priority.

Record direct URL and checked on: YYYY-MM-DD. Profile category hints are routing aids only; current official labels control. Never rely on stored dates, fees, category numbers, or remembered requirements.

5. Compare criteria and winner evidence

Read references/evidence-policy.md and references/criteria-crosswalk.json. Display each award's official criterion name; use normalized dimensions only for cross-award comparison.

Map every criterion to concrete project evidence using Strong, Partial, Weak, or Unknown. Do not award alignment for generic claims.

Past winners are optional evidence. Use $design-award-search or a small verified official-source sample. State sample size, years, category, and limitations. Describe observable past-winner trends, never hidden jury preferences.

6. Score and rank

Read references/matching-framework.md. Score five dimensions from 0 to 5 with one evidence sentence per rating. Prepare input using examples/match-input.example.json, then run:

python scripts/score_award_matches.py examples/match-input.example.json --pretty

Keep separate:

• Fit score: weighted strategic compatibility, 0–100.
• Evidence confidence: High, Medium, or Low.
• Eligibility: Eligible, Unknown, or Ineligible.

Do not change the numeric fit because confidence is low. Cap Unknown at Conditional.

7. Report

Follow references/output-template.md. Lead with the primary target and reason. Include:

• project profile and assumptions;
• ranked shortlist;
• criterion alignment for top options;
• eligibility, timing, fee, and mandatory-obligation risks;
• winner trends only when sufficiently supported;
• actions that resolve uncertainty or strengthen fit;
• official sources with checked dates.

Prefer one primary target, one secondary target, and one conditional or stretch option.

Decision Rules

• Exact program, track, and category fit outranks brand prestige.
• Confirmed ineligibility overrides any fit score.
• Current official rules override profiles, archived pages, third-party summaries, and memory.
• Published criteria control criteria alignment.
• Winner trends may refine but never replace published criteria.
• An open brief such as James Dyson has no exact product-category match; score its category relation as broad.
• A missing universal rubric, as with Core77, lowers criteria-evidence confidence; do not invent criteria.
• If candidates are within five points, prefer higher confidence and fewer unresolved gates.
• A high fit score is not a forecast of winning.

Fallback and Compliance

• Without live web access, use only user-supplied official documents and mark every dynamic gate unverified.
• If no official category fits, report No defensible match.
• If fewer than three comparable winners are verified, omit trends or label them anecdotal.
• Distinguish current cycle closed from structural ineligibility.
• Use public pages without login; do not crawl or mirror galleries.
• Store no official images, full descriptions, raw payloads, or content-derived embeddings.
• For James Dyson, use only public official sources; do not reintroduce removed private records.
• Quote minimally and avoid legal, financial, or contractual certainty.

Example Invocations

• 使用 $design-award-match，基于附件匹配最合适的设计奖、赛道和类别，并输出适配度、资格风险和申报优先级。
• 使用 $design-award-match，比较 iF、Red Dot、IDEA、DIA 和 Core77 中哪个项目路径最适合这个学生概念。
• Use $design-award-match to rank the supported award routes for this project and list every unresolved live eligibility gate.