Does this skill support biological ontologies?

Yes, it integrates with Bionty to validate and standardize terms for genes, cell types, tissues, diseases, and other biological entities.

Is it compatible with existing bioinformatics pipelines?

Yes, it provides integration patterns for Nextflow, Snakemake, and popular analysis tools like scVI-tools and Vitessce.

How does it improve research reproducibility?

It automatically tracks the lineage of data artifacts, versioning both the data and the code that produced it while capturing the environment settings.

Can I use this with cloud storage providers?

Absolutely, LaminDB supports local filesystems, AWS S3, Google Cloud Storage, and S3-compatible endpoints like MinIO.

What is the primary purpose of the LaminDB skill?

The LaminDB skill helps manage biological datasets to make them queryable, traceable, and FAIR-compliant using a unified Python API within AI workflows.

LaminDB Biology Data Management

Name: LaminDB Biology Data Management
Author: jimmc414

byjimmc414

•

324

•

데이터 과학 및 ML

Manages biological datasets with automated lineage tracking, ontology-based curation, and FAIR-compliant data lakehouse capabilities.

LaminDB for Claude Code empowers AI-driven scientific discovery by providing a robust framework for managing complex biological data like scRNA-seq and spatial transcriptomics. This skill enables Claude to handle data provenance, version control, and standardized annotations using biological ontologies via Bionty, ensuring all research is findable, accessible, interoperable, and reusable (FAIR). It is an essential tool for bioinformaticians and data scientists looking to build queryable data lakehouses that integrate seamlessly with workflow managers like Nextflow and MLOps platforms like Weights & Biases.

주요 기능

01Automatic lineage tracking from raw data to final analysis results

02Ontology-based validation for genes, cell types, and diseases via Bionty

03Version control for datasets, code transforms, and computational environments

04Seamless integration with workflow managers and single-cell analysis tools

05324 GitHub stars

06Unified query interface for local and cloud-based data lakehouses

사용 사례

01Creating a centralized, queryable repository for multi-modal biological research

02Building a traceable and reproducible pipeline for spatial transcriptomics

03Standardizing scRNA-seq metadata against canonical biological ontologies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jimmc414/kosmos lamindb

For use in Claude.ai and ChatGPT

Download Skill