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按技能名称、说明或 GitHub 作者匹配的结果。

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显示第 625–648 个,共 2,686 个

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GitHub

async-python-patterns

lifangda

Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking oper...

GitHub

pydeseq2

lifangda

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

GitHub

zarr-python

lifangda

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

GitHub

biopython

lifangda

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,...

GitHub

bioservices

lifangda

Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct...

GitHub

omero-integration

lifangda

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

GitHub

pyopenms

lifangda

Mass spectrometry toolkit (OpenMS Python). Process mzML/mzXML, peak picking, feature detection, peptide ID, proteomics/metabolomics workflows, for LC-MS/MS analysis.

GitHub

gget

lifangda

CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads....

GitHub

dnanexus-integration

lifangda

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

GitHub

datamol

lifangda

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, paral...

GitHub

async-expert

martinholovsky

Expert in asynchronous programming patterns across languages (Python asyncio, JavaScript/TypeScript promises, C# async/await, Rust futures). Use for concurrent programming, event loops, async patterns, error ha...

GitHub

python

martinholovsky

Backend services development with Python emphasizing security, performance, and maintainability for JARVIS AI Assistant

GitHub

fastapi-expert

martinholovsky

Expert FastAPI developer specializing in production-ready async REST APIs with Pydantic v2, SQLAlchemy 2.0, OAuth2/JWT authentication, and comprehensive security. Deep expertise in dependency injection, backgro...

GitHub

fastmcp

ovachiever

Build MCP servers in Python with FastMCP framework to expose tools, resources, and prompts to LLMs. Supports storage backends (memory/disk/Redis), middleware, OAuth Proxy, OpenAPI integration, and FastMCP Cloud...

GitHub

python-performance-optimization

ovachiever

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

GitHub

flask-python

Mindrally

Guidelines for Flask Python development with best practices for blueprints, RESTful APIs, and application factories.

GitHub

python

Mindrally

Expert in Python development with best practices across web, data science, and automation

GitHub

scrapy-web-scraping

Mindrally

Expert guidance for building web scrapers and crawlers using the Scrapy Python framework with best practices for spider development, data extraction, and pipeline management.

GitHub

microservices

Mindrally

Guidelines for building production-grade microservices with FastAPI/Python and Go, covering serverless patterns, clean architecture, observability, and resilience.

默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。

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