scientific-generation
Generate scientific code, protocols, and domain-specific text with quality control
Best use case
scientific-generation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate scientific code, protocols, and domain-specific text with quality control
Teams using scientific-generation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scientific-generation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scientific-generation Compares
| Feature / Agent | scientific-generation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate scientific code, protocols, and domain-specific text with quality control
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Scientific Generation & Writing ## Purpose Generate high-quality scientific code, experimental protocols, and domain-specific text outputs. ## Key Datasets - **Tiny-Codes** (nampdn-ai/tiny-codes): 1.6M code snippets across 11 languages (Python, TypeScript, JavaScript, Ruby, Rust, C++, Java, Go, etc.) for code generation benchmarks - **Mental Health Counseling** (Amod/mental_health_counseling_conversations): Therapeutic conversation corpus for empathetic response generation ## Generation Types - **Code generation**: Scientific computing scripts, data pipelines, analysis workflows - **Protocol generation**: Experimental procedures, assay protocols, clinical workflows - **Report generation**: Lab reports, progress reports, technical memos - **Response generation**: Literature-based answers, educational explanations ## Protocol 1. **Requirements analysis** — Define output specifications, constraints, and quality criteria 2. **Template selection** — Choose appropriate template or structure 3. **Content generation** — Generate with domain-specific knowledge 4. **Quality validation** — Check correctness, completeness, and adherence to standards 5. **Iteration** — Refine based on validation feedback ## Rules - Generated code must include error handling and documentation - Scientific protocols must specify reagents, equipment, and safety precautions - All generated content must be factually grounded - Flag any assumptions or simplifications made during generation - For therapeutic/counseling contexts, follow ethical guidelines
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