Agent skill

Design Information Prep

by SeanJ1ang in SeanJ1ang/design-judge-skills

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.

Apache-2.0Auto-check: notesLegal & Compliance

Install Design Information Prep

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/design-information-prep .claude/skills/design-information-prep && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .claude/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/design-information-prep .agents/skills/design-information-prep && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .agents/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/design-information-prep .cursor/skills/design-information-prep && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .cursor/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/SeanJ1ang/design-judge-skills.git --path skills/design-information-prep

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/design-information-prep .gemini/skills/design-information-prep && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .gemini/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/design-information-prep .github/skills/design-information-prep && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .github/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install SeanJ1ang/design-judge-skills design-information-prep --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/SeanJ1ang/design-judge-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/design-information-prep .opencode/skills/design-information-prep && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep into .opencode/skills/design-information-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "design-information-prep", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: design-information-prep
GitHub stars: 712
Token cost: ~1.8k tokens
SKILL.md length: 750 words
Files: 30 (incl. scripts, references)
Skills in repo: 7
Repo updated: Aug 24, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

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.

Works in 6 steps: Lock the target → Build the project dossier → Prepare an award-specific evidence packet → …
A user asks to prepare
SKILL.md covers Purpose, Runtime Boundary, Input Contract and Workflow, plus 3 more sections
Calls python

What it does

Design Information Prep is an agent skill from SeanJ1ang/design-judge-skills. 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…

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 33 other files, including scripts and reference files (for example `README.md`, `README_EN.md` and `agents/openai.yaml`).

It sits in Legal & Compliance, covering Intellectual property and Design review and critique. The repository describes itself as: Evidence-driven Agent Skills for design award research, evaluation, award matching, entry writing, and submission readiness. The licence is Apache-2.0.

When your agent uses it

A user asks to prepare
Validate application text for iF
Red Dot Product Design
GOOD DESIGN AWARD Japan

Example prompts

“/design-information-prep”

Requirements

Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Lock the target
Build the project dossier
Prepare an award-specific evidence packet
Draft field by field
Validate the draft
Report

What it can do on your machine

Read from SKILL.md and the folder at commit abf53e6. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Design Information Prep loads about 1.8k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 177 tokens; SKILL.md has 750 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~177
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteMentions a .env fileSKILL.md:16

- Do not connect to Supabase, read `.env`, query winner databases, or place full winner descriptions in model context.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from SeanJ1ang/design-judge-skills at commit abf53e6, republished under its Apache-2.0 licence (© SeanJ1ang). 750 words, ~1,780 tokens.

Download SKILL.mdSave it as .claude/skills/design-information-prep/SKILL.md (or your agent's skills folder). This skill also uses 29 other files; get the full folder from GitHub.

name: design-information-prep
description: 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.

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.

Show full SKILL.md (276 more words)Show less

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:

target award, route, cycle, language, and rule freshness;
prepared field text with limit usage;
evidence coverage and assumptions;
missing information as concise user questions;
fields requiring confirmation;
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.

Files

SKILL.md and 29 other files (scripts, references) in skills/design-information-prep of SeanJ1ang/design-judge-skills.

SKILL.md
README.md
README_EN.md
agents/openai.yaml
examples/idea-entry-output.example.json
examples/project-dossier.example.json
references/awards/core77.json
references/awards/dia.json
references/awards/epda.json
references/awards/good-design-japan.json
references/awards/idea.json
references/awards/if-design.json
references/awards/if-student.json
references/awards/james-dyson.json
references/awards/k-design.json
references/awards/red-dot-product.json
references/entry-output-schema.json
… and 13 more

Open the folder on GitHubat commit abf53e6

Compare with similar skills

Design Information Prep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Design Information Prep compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Design Information Prep this skillSeanJ1ang/design-judge-skills	712	—	~1.8k	Automated safety check: Notes	Apache-2.0	1 mo ago
Paper to Chinese Patent DrafterYuan1z0825/nature-skills	47k	1 repos	~1.1k	Automated safety check: Pass	Apache-2.0	today
Paper To Cn Patentsnipp-zha/Paper-to-patent-Skill	107	1 repos	~959	Automated safety check: Pass	None	3 mo ago
Patent Examinegfodor/legal-skills	393	—	~4.8k	Automated safety check: Pass	GPL-3.0	1 mo ago
Patent Auditgfodor/legal-skills	393	—	~2.9k	Automated safety check: Pass	GPL-3.0	1 mo ago
Replica BrandJakeschincariol/replica-skill	1.4k	—	~1.1k	Automated safety check: Pass	MIT	7 days ago

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Questions about Design Information Prep

What does Design Information Prep 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. Design Information Prep is an agent skill from SeanJ1ang/design-judge-skills. 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.

When should I use Design Information Prep?

Design Information Prep fits situations like: A user asks to prepare; validate application text for iF; red Dot Product Design; GOOD DESIGN AWARD Japan.

How do I install Design Information Prep in Claude Code?

Run `npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a claude-code`. Or copy the skill folder (skills/design-information-prep in SeanJ1ang/design-judge-skills) into .claude/skills/design-information-prep in your project. Claude Code loads it when a task matches its description.

How do I install Design Information Prep in Codex?

Run `npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a codex`. Or copy the skill folder (skills/design-information-prep in SeanJ1ang/design-judge-skills) into .agents/skills/design-information-prep in your project. Codex loads it when a task matches its description.

Can I use Design Information Prep in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/design-information-prep, .gemini/skills/design-information-prep, .github/skills/design-information-prep and .opencode/skills/design-information-prep in your project.

What does Design Information Prep need to run?

Going by SKILL.md and its folder, Design Information Prep needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Design Information Prep access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Design Information Prep safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Design Information Prep use?

Design Information Prep is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Design Information Prep use?

About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Design Information Prep?

Skills that share tags, products or a category with Design Information Prep: Paper to Chinese Patent Drafter (Yuan1z0825/nature-skills, 47k stars), Paper To Cn Patent (snipp-zha/Paper-to-patent-Skill, 107 stars), Patent Examine (gfodor/legal-skills, 393 stars) and Patent Audit (gfodor/legal-skills, 393 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Design Information Prep?

SeanJ1ang (a GitHub user) maintains it in SeanJ1ang/design-judge-skills, which has 712 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 24, 2026.

Source: SeanJ1ang/design-judge-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Design Information Prep

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Design Information Prep

Purpose

Runtime Boundary

Input Contract

Workflow

1. Lock the target

2. Build the project dossier

3. Prepare an award-specific evidence packet

4. Draft field by field

5. Validate the draft

6. Report

Supported Award Specifications

Decision Rules

Example Invocations

Files

Compare with similar skills

Similar skills

More from SeanJ1ang/design-judge-skills

Related

Questions about Design Information Prep