Generate scientific code, protocols, and domain-specific text with quality control
--- name: scientific-generation description: Generate scientific code, protocols, and domain-specific text with quality control --- # 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--- name: scientific-generation description: Generate scientific code, protocols, and domain-specific text with quality control --- # 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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.