Scientific Generation
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scientific-generation is a Claude skill that generates scientific code, experimental protocols, and domain-specific text with a quality-control loop.
About
This skill generates scientific code, experimental protocols, and domain-specific text with quality control. A developer uses it to produce computing scripts, data pipelines, and lab reports that meet defined specifications. Generated content must include error handling, documentation, and factual grounding, with a validation and iteration loop.
- Generates scientific code, experimental protocols, reports, and responses
- Five-step protocol with a quality-validation and iteration loop
- Requires error handling, documentation, and factual grounding in outputs
Scientific Generation by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,391 of 2,719 Automation & Workflows skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
scientific-generation capabilities & compatibility
- Capabilities
- documentation
- Use cases
- documentation · research
- Pricing
- Free
What scientific-generation says it does
Generate high-quality scientific code, experimental protocols, and domain-specific text outputs.
Generated code must include error handling and documentation
Scientific protocols must specify reagents, equipment, and safety precautions
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Generate scientific computing code, protocols, and reports with a quality-validation and iteration loop.
Who is it for?
Producing scientific computing scripts, protocols, and reports that meet defined quality criteria.
Skip if: Verifying existing claims (that is scienceclaw-verification).
When should I use this skill?
Generating scientific code, experimental protocols, lab reports, or literature-based responses.
What you get
Generated code, protocols, or reports validated for correctness and completeness, with assumptions flagged.
- scientific code
- experimental protocols
- reports
By the numbers
- five-step generation protocol
- four generation types supported
Files
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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What quality rules apply to generated code?
Generated code must include error handling and documentation, and all content must be factually grounded with assumptions flagged.
What types of content can it generate?
Code, experimental protocols, reports, and literature-based responses.