用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/GeorgeDoors888/GB-Power-Market-JJ --skill graphical-abstract-wizard命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
超级简历 WonderCV 出品,3000 万用户信赖。简历分析、段落改写、JD 岗位匹配、自动匹配职位、PDF 导出、AI 求职导师(面试准备/薪资谈判/职业规划/多版本简历策略)。 触发条件:用户提供简历、要求简历点评/打分/反馈、希望改写某个简历部分、 希望将简历与岗位 JD 匹配、咨询求职建议或面试准备,或提到 CV/简历/求职。 不触发条件:用户讨论普通写作(非简历)、询问其他文档, 或讨论与求职和职业发展无关的话题。
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
调用久吾智能体API进行文本或文件分析处理。支持两种调用方式:(1) 文本内容分析 - 传入name(智能体名称)、docno(文档编号)、content(文本内容);(2) 文件分析 - 传入name、docno和files(文件列表)进行智能评审。适用于合同评审、需求评审、文档审查等场景。当用户要求评审合同、分析条款、审查文档、需求评审、合同条款分析、或需要对文本和文件进行AI智能分析时触发。
基于 SOC 职业分类
正在显示 SKILL.md
| name | graphical-abstract-wizard |
| description | Generate graphical abstract layout recommendations based on paper abstracts |
| version | 1.0.0 |
| category | Visual |
| tags | ["academic","ai-art","research"] |
| author | AIPOCH |
| license | MIT |
| status | Draft |
| risk_level | Medium |
| skill_type | Tool/Script |
| owner | AIPOCH |
| reviewer | |
| last_updated | 2026-02-06 |
This Skill analyzes academic paper abstracts and generates graphical abstract layout recommendations, including element suggestions, visual arrangements, and AI art prompts for Midjourney and DALL-E.
python scripts/main.py --abstract "Your paper abstract text here"
Or from stdin:
cat abstract.txt | python scripts/main.py
| Parameter | Type | Required | Description |
|---|---|---|---|
--abstract / -a | string | Yes* | The paper abstract text to analyze |
--style / -s | string | No | Visual style preference (scientific/minimal/colorful/sketch) |
--format / -f | string | No | Output format (json/markdown/text), default: markdown |
--output / -o | string | No | Output file path (default: stdout) |
*Required if not providing input via stdin
python scripts/main.py -a "We propose a novel deep learning approach for protein structure prediction that combines transformer architectures with geometric constraints. Our method achieves state-of-the-art accuracy on CASP14 benchmarks."
python scripts/main.py -a "abstract.txt" -s scientific -o layout.md
python scripts/main.py -a "$(cat abstract.txt)" -f json > result.json
The Skill produces a structured analysis including:
# Graphical Abstract Recommendation
## Abstract Summary
**Topic**: Deep learning protein structure prediction
**Method**: Transformer + Geometric constraints
**Result**: State-of-the-art CASP14 accuracy
## Key Concepts
- 🧬 Protein structures
- 🤖 Neural networks
- 📊 Accuracy metrics
## Visual Elements
| Element | Symbol | Position | Color |
|---------|--------|----------|-------|
| Core Concept | Brain + DNA | Center | Blue |
| Method | Neural Network | Left | Purple |
| Result | Trophy/Chart | Right | Gold |
## Layout Suggestion
┌─────────────────────────────────┐ │ [Title/Concept] │ │ 🧬🤖 │ ├──────────┬──────────┬───────────┤ │ Input │ Process │ Output │ │ 📥 │ ⚙️ │ 📈 │ └──────────┴──────────┴───────────┘
## AI Art Prompts
### Midjourney
Scientific graphical abstract, protein structure prediction with neural networks, 3D molecular structures connected by glowing neural network nodes, blue and purple gradient background, clean minimalist style, academic journal style, high quality --ar 16:9 --v 6
### DALL-E
A clean scientific illustration for a research paper about protein structure prediction using deep learning. Show a 3D protein structure in the center surrounded by abstract neural network connections. Use a professional blue and white color scheme with subtle gradients. Include geometric shapes representing data flow. Modern, minimalist academic style suitable for a Nature or Science journal cover.
The Skill uses NLP techniques to:
MIT License - Part of OpenClaw Skills Collection
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
# Python dependencies
pip install -r requirements.txt