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.
npx skills add https://github.com/SeanJ1ang/design-judge-skills --skill design-information-prep
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;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.
Save the dossier as structured JSON and run:
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.
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.
Prepare machine-checkable output using references/entry-output-schema.json. Include used_fact_ids for every drafted field.
Run:
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.
Follow references/output-template.md. Return:
The references/awards/ directory contains versioned public-field specifications for:
Validate all specifications after editing:
python scripts/validate_field_specs.py --pretty
Ready requires every required field to pass limits and provenance checks.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.Extract cognitive patterns and thinking fingerprints from any text. Use this skill when the user wants to analyze how someone thinks, understand cognitive style, profile writing or speech patterns, compare thinking styles between people, asks "what's my thinking style", "analyze how this person reasons", "cognitive profile", "thinking pattern", "DHDNA", "digital DNA", or wants to understand the mind behind any text. Also trigger when the user provides text and wants deeper insight into the author's reasoning patterns, decision-making style, or cognitive signature.
GSAP animation reference for HyperFrames. Covers gsap.to(), from(), fromTo(), easing, stagger, defaults, timelines (gsap.timeline(), position parameter, labels, nesting, playback), and performance (transforms, will-change, quickTo). Use when writing GSAP animations in HyperFrames compositions.
配图助手 - 把文章/模块内容转成统一风格、少字高可读的 16:9 信息图提示词;先定“需要几张图+每张讲什么”,再压缩文案与隐喻,最后输出可直接复制的生图提示词并迭代。
| YouTube clip generation and editing with automated workflows — pull source video, slice highlights, add captions, and export.
Best practices for writing Remotion animations that stay intuitive for agents and editable in Remotion Studio Visual Mode.
YouTube transcript extraction and content reformatting: given a YouTube video URL, opens the video's transcript panel, extracts all timestamped segments, and transforms the raw transcript into summaries, chapter outlines, Twitter/X threads, blog posts, or notable quotes. Use when the user shares a YouTube URL or video link, asks to summarize a video, get a transcript, extract content from a YouTube video, get YouTube captions, extract YouTube captions, download YouTube captions, transcribe YouTube video, YouTube video to text, make a thread from YouTube, YouTube to blog post, YouTube to article, pull transcript from YouTube, YouTube content extraction, convert YouTube to text, video to transcript. Also applies when user wants to reformat any YouTube video content into structured output (chapters, threads, blog articles, key quotes).
跨境电商全链路自动化工具。集成1688采集、智能清洗、多平台上架(微信小店/Shopify/TikTok)、推广方案(关键词/竞品分析/广告文案)、短视频创作(MoviePy竖屏视频)、一键代发、爆品挖掘(趋势聚合+6维评分)、闲鱼二手选品捡漏(品牌识别/虚标过滤/捡漏评分/价格监控)、全自动流水线(挖掘→采集→清洗→上架→推广→视频)。
生成历史名人现代访谈短视频文案,通过古今反差与网络热梗的爆笑结合,创作具有传播力的虚构趣味内容
Take seanj1ang/design-information-prep from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.