Agent skill · data analytics · aiskillstore
lamindb
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Why this skill is useful
Provides domain-specific knowledge on managing biological datasets and integrating with computational workflows that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
LaminDB Overview LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API. Core Value Proposition: Queryability: Search and filter datasets by metadata, features, and ontology terms Traceability: Automatic lineage tracking from raw data through analysis to results Reproducibility: Version control for data, code, and environment FAIR Compliance: Standardized annotations using biological ontologies When to Use This Skill Use this skill when: Managing biological datasets: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data Tracking computational workflows: Notebooks, scripts, pipeline execution (Nextflow, Snakemake, Redun) Curating and validating data: Schema validation, standardization, ontology-based annotation Working with biological ontologies: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty) Building data lakehouses: Unified query interface across multiple datasets Ensuring reproducibility: Automatic versioning, lineage tracking, environment capture Integrating ML pipelines: Connecting with Weights & Biases, MLflow, HuggingFace, scVI-tools Deploying data infrastructure: Setting up local or cloud-based data management systems Collaborating on datasets: Sharing curated, annotated data with standardized metadata Core Capabilities LaminDB provides six interconnected capability areas, each documented in detail in the references folder. 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/lamindb