Creator · SeanJ1ang
Last updated · Sep 4, 2026
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
Sandbox only
Install targets
Codex install prompt
Install the "design-information-prep" agent skill from https://github.com/SeanJ1ang/design-judge-skills/tree/main/skills/design-information-prep. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"seanj1ang-design-information-prep","task":"Install design-information-prep","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep
Maintenance
fresh
15d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
1.0K
77/100 Quality · 78/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
1.0K GitHub stars
Repo activity
1.0K stars, 155 forks
Maintenance
15d since push
License
Apache-2.0
Install
npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add SeanJ1ang/design-judge-skills --skill design-information-prepDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20design-information-prep%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20design-information-prep%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/seanj1ang-design-information-prep/install
Agent should check
Copy prompt
Task: Use design-information-prep in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20design-information-prep%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/seanj1ang-design-information-prep/install
Install command: npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/seanj1ang-design-information-prep/install
LLM text format
/api/skills/seanj1ang-design-information-prep/install?format=text
Find alternatives
/api/skills/search?q=design-information-prep&limit=3
Agent prompt
Use design-information-prep for this task. Review https://www.openagentskill.com/api/skills/seanj1ang-design-information-prep/install, then install with: npx skills add SeanJ1ang/design-judge-skills --skill design-information-prepRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/seanj1ang-design-information-prep
LLM text
/api/registry/manifest/seanj1ang-design-information-prep?format=text
Install alias
/api/registry/install/seanj1ang-design-information-prep
Recommend
/api/registry/recommend?task=Use%20design-information-prep%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS1.0K GitHub stars
Stars/forks activity
INFO1.0K stars, 155 forks; issue activity unavailable in current metadata
Recent maintenance
PASS15d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Alternative shortlist
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--- 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](references/evidence-policy.md) and [references/project-dossier-schema.json](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](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](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.`
Source provenance
Decision snapshot
1,021 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for design-information-prep, ready for a manual X post.
design-information-prep: Extract evidence-grounded project facts from user-provided design attachments, identify missi... 1.0K stars https://www.openagentskill.com/skills/seanj1ang-design-information-prep?ref=x
Listing + install path for design-information-prep: https://www.openagentskill.com/skills/seanj1ang-design-information-prep?ref=x Install: npx skills add SeanJ1ang/design-judge-skills --skill design-information-prep
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Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness