Design Information Prep logo

Design Information Prep

Community
SeanJ1ang
design-information-prep

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. Use when a user asks to prepare, draft, adapt, translate, or validate application text for iF, iF Student, Red Dot Product Design, IDEA, DIA, K-Design, GOOD DESIGN AWARD Japan, Core77, James Dyson, or EPDA. Also use to build a reusable project dossier from briefs, decks, reports, manuals, patents, research, images, or prior application materials. Do not use for award selection alone, winner retrieval, design-quality scoring, final file-format auditing, or winning-probability prediction.

Overview

PublisherSeanJ1ang
Repositorydesign-judge-skills
Skill namedesign-information-prep
Stars
712
Forks
102
Bundled files
26
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 26 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by SeanJ1ang on GitHub. Read the source before you install it.

Installation

Install the Design Information Prep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git /tmp/design-judge-skills
mkdir -p .claude/skills
cp -r /tmp/design-judge-skills/skills/design-information-prep .claude/skills/design-information-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Information Prep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Design Information Prep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Design Information Prep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design Information Prep

Purpose

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.

Runtime Boundary

  • Treat user attachments and explicit user confirmations as the only sources of project facts.
  • Read local award field specifications for field names, limits, routing, and drafting instructions.
  • Do not connect to Supabase, read .env, query winner databases, or place full winner descriptions in model context.
  • Use optional aggregate benchmark profiles only for coverage prompts such as what evidence to look for. Never use them as project facts, prose templates, hidden judging preferences, or winning probabilities.
  • Verify current official rules at request time. Stored specifications record a checked date, not permanent truth.

Input Contract

Accept PDFs, presentations, documents, spreadsheets, images, videos, structured JSON, or plain text. Determine or request:

  • exact award, cycle, route, and language;
  • applicant type and project maturity when they affect routing;
  • all user-authorized project materials;
  • confidentiality or publication restrictions;
  • whether the user wants a dossier, missing-information audit, draft fields, translation, or final text validation.

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.

Workflow

1. Lock the target

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.

2. Build the project dossier

Read references/evidence-policy.md and references/project-dossier-schema.json. Extract canonical facts into facts records containing:

  • value;
  • status: supported, inferred, confirmed_by_user, or missing;
  • confidence;
  • attachment evidence and locator;
  • whether user confirmation is required.

Preserve contradictions as separate findings. Do not choose a convenient value without reporting the conflict. Mark unavailable facts missing; never fill them from general knowledge or a past winner.

3. Prepare an award-specific evidence packet

Save the dossier as structured JSON and run:

powershell
python scripts/prepare_entry_packet.py `
  --dossier examples/project-dossier.example.json `
  --award idea `
  --route general `
  --pretty

The packet identifies ready fields, missing essential facts, available evidence, limits, and drafting instructions. Ask only the questions that block required fields. Continue with partial output when the user prefers, labeling every unresolved field.

4. Draft field by field

Use only facts listed in each field's prepared evidence packet. Follow the official field purpose rather than forcing one generic description into every form.

  • Lead with the answer, not promotional framing.
  • Prefer specific mechanisms and outcomes over unverified superlatives.
  • Distinguish measured outcomes from intended benefits.
  • Preserve units, denominators, dates, maturity, and uncertainty.
  • Do not convert an inference into a confirmed claim through fluent wording.
  • Count words and characters according to the field specification.
  • Keep translations semantically aligned; do not introduce new claims in one language.

Prepare machine-checkable output using references/entry-output-schema.json. Include used_fact_ids for every drafted field.

5. Validate the draft

Run:

powershell
python scripts/validate_entry_output.py `
  --dossier examples/project-dossier.example.json `
  --entry examples/idea-entry-output.example.json `
  --pretty

Resolve every Blocker before presenting a field as submission-ready. Treat unsupported or inferred claims awaiting confirmation as Important. The validator checks required fields, route alignment, list limits, word/character limits, and fact provenance; it does not verify scientific truth or live portal behavior.

6. Report

Follow references/output-template.md. Return:

  1. target award, route, cycle, language, and rule freshness;
  2. prepared field text with limit usage;
  3. evidence coverage and assumptions;
  4. missing information as concise user questions;
  5. fields requiring confirmation;
  6. validation decision and remaining findings.

Supported Award Specifications

The references/awards/ directory contains versioned public-field specifications for:

  • iF DESIGN AWARD;
  • iF DESIGN STUDENT AWARD;
  • Red Dot Award: Product Design;
  • IDEA;
  • Design Intelligence Award, with separate Product and Concept routes;
  • K-Design Award;
  • GOOD DESIGN AWARD Japan;
  • Core77 Design Awards;
  • James Dyson Award;
  • European Product Design Award, with Professional and Student routes.

Validate all specifications after editing:

powershell
python scripts/validate_field_specs.py --pretty

Decision Rules

  • Current official rules override stored specifications, previous cycles, winner pages, and memory.
  • Missing attachment evidence never becomes a supported fact by inference.
  • A field may be drafted provisionally from an inferred fact only when clearly labeled and confirmed before final submission.
  • Public winner descriptions are not evidence of the user's design and are not application-form ground truth.
  • Evaluation criteria are not separate form fields unless official entry materials explicitly expose them as fields.
  • Ready requires every required field to pass limits and provenance checks.
  • A completed text audit does not prove portal acceptance or legal clearance.

Example Invocations

  • 使用 $design-information-prep,从附件建立作品信息母稿,并生成 IDEA 需要填写的全部英文文字字段。
  • 使用 $design-information-prep,检查这套材料是否足以填写 DIA 概念组;不要补造缺失的市场或测试数据。
  • Use $design-information-prep to adapt this project dossier to iF and Red Dot while preserving evidence links and character limits.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Design Information Prep AI skill do?

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. Use when a user asks to prepare, draft, adapt, translate, or validate application text for iF, iF Student, Red Dot Product Design, IDEA, DIA, K-Design, GOOD DESIGN AWARD Japan, Core77, James Dyson, or EPDA. Also use to build a reusable project dossier from briefs, decks, reports, manuals, patents, research, images, or prior application materials. Do not use for award selection alone, winner retrieval...

Why use Design Information Prep on TypingMind?

Because you install it once and use it with any model. Design Information Prep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Design Information Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Design Information Prep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Design Information Prep?

As many as you like. As long as a model supports skills, you can use Design Information Prep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Design Information Prep AI skill free?

Yes. It is published on GitHub by SeanJ1ang under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇