Agent skill · data analytics · k-dense-ai
lamindb
Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
Why this skill is useful
Provides specific commands and workflows for managing biological datasets and lineage tracking that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 8k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
LaminDB Overview LaminDB is an open-source, lineage-native lakehouse for biology. It makes datasets and models queryable, traceable, validated, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) while storing data in open formats across local filesystems, S3, GCS, Hugging Face, SQLite, and Postgres. Core Value Proposition: Queryability: Search and filter artifacts, records, runs, features, schemas, and collections Traceability: Track inputs, outputs, parameters, source code, and environments for notebooks, scripts, functions, and pipelines Validation: Curate DataFrame, AnnData, SpatialData, TileDB-SOMA, Parquet, Zarr, and other biological formats with schemas FAIR Compliance: Standardize annotations with Bionty-backed ontologies and custom registries Change management: Organize work with projects, branches, spaces, collections, and saved notes or plans 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, functions, shell scripts, and 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, Hugging Face, Lightning, 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 k-dense-ai/lamindb