biopython
Description
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
How to Use
- Visit the GitHub repository to get the SKILL.md file
- Copy the file to your project root or .cursor/rules directory
- Restart your AI assistant or editor to apply the new skill
Tags
About biopython
biopython is an AI skill in the ml-data category, designed to help developers and users work more effectively with AI tools. Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
This skill has earned 2,800 stars on GitHub, reflecting strong community adoption and trust. It is compatible with claude.
Key Capabilities
Why Use biopython
Adding biopython to your AI workflow can significantly enhance your productivity in ml-data tasks. With pre-defined prompt templates and best practices, this skill helps AI assistants better understand your requirements and deliver more accurate responses.
Whether you use claude, you can easily integrate this skill into your existing development environment.
Explore More ml-data Skills
Discover more AI skills in the ml-data category to build a comprehensive AI skill stack.
Related Skills
langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
data-storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure.
pyhealth
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, ND
biomni
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GW