Scientific Schematic Generation
技能 已验证 活跃Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
To automatically generate publication-quality scientific diagrams and schematics from natural language descriptions, ensuring accuracy, clarity, and adherence to scientific communication standards.
功能
- AI-powered scientific diagram generation
- Iterative refinement based on quality review
- Document-type specific quality thresholds
- Publication-ready output formatting
- Detailed usage and error handling
使用场景
- Creating figures for scientific papers and presentations
- Visualizing complex scientific concepts, pathways, or architectures
- Generating custom diagrams for grant proposals or technical reports
- Automating the creation of consistent visual elements in research documentation
非目标
- Editing existing diagrams
- Generating diagrams without natural language input
- Replacing manual drafting for highly bespoke or artistic illustrations
- Providing real-time interactive diagram editing
实践
- Scientific Communication
- AI Prompt Engineering
- Documentation
先决条件
- Python 3.11+ environment
- OpenRouter API key (via environment variable or parameter)
Code Execution
- info:LoggingThe script provides verbose logging when the -v flag is used, detailing API calls and progress, but does not write to a separate audit file for destructive actions as it performs none.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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