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
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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
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📦 Ships with scientific-agent-skills
</> SKILL.md
lamindb.SKILL.md
---name: lamindb
description: 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.
license: Apache-2.0 license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# 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:
- Create and version artifacts from files or Python objects
- Track notebook/script execution with `ln.track()` and `ln.finish()`
- Track function workflows with `@ln.flow()` and `@ln.step()`
- Annotate artifacts with records, ulabels, projects, and typed features
- Visualize data lineage graphs with `artifact.view_lineage()`
- Query by provenance (find all outputs from specific code/inputs)
**Reference:** `references/core-concepts.md` - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.
### 2. Data Management and Querying
**Query capabilities:**
- Registry exploration and lookup with auto-complete
- Single record retrieval with `get()`, `one()`, `one_or_none()`
- Filtering with comparison operators (`__gt`, `__lte`, `__contains`, `__startswith`)
- Feature-based queries, including expression-style queries with `Feature` objects
- Cross-registry traversal with double-underscore syntax
- Full-text search across registries
- Advanced logical queries with `ln.Q` objects (AND, OR, NOT)
- Streaming large datasets without loading into memory
**Key workflows:**
- Browse artifacts with filters and ordering
- Query by features, creation date, creator, size, etc.
- Stream large files in chunks or with array slicing
- Organize data with hierarchical keys
- Group artifacts into collections
**Reference:** `references/data-management.md` - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.
### 3. Annotation and Validation
**Curation process:**
1. **Validation**: Confirm datasets match desired schemas
2. **Standardization**: Fix typos, map synonyms to canonical terms
3. **Annotation**: Link datasets to metadata entities for queryability
**Schema types:**
- **Flexible schemas**: Validate only known columns, allow additional metadata
- **Strict schemas**: Complete control over structure and values
**Supported data types:**
- DataFrames (Parquet, CSV)
- AnnData (single-cell genomics)
- MuData (multi-modal)
- SpatialData (spatial transcriptomics)
- TileDB-SOMA (scalable arrays)
**Key workflows:**
- Define features and schemas for data validation
- Use `DataFrameCurator`, `AnnDataCurator`, `SpatialDataCurator`, or `TiledbsomaExperimentCurator` for validation
- Standardize values with `.cat.standardize()`
- Map to ontologies with `.cat.add_ontology()`
- Save curated artifacts with schema linkage
- Query validated datasets by features
**Reference:** `references/annotation-validation.md` - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.
- Bionty module: included in the LaminDB docs and available as `uv pip install 'bionty==2.4.0'`
- Optional modules: pin reviewed releases for wetlab or clinical schema modules rather than installing floating latest versions
**Instance types:**
- Local SQLite (development)
- Cloud storage + SQLite (small teams)
- Cloud storage + PostgreSQL (production)
**Storage options:**
- Local filesystem
- AWS S3 with configurable regions and permissions
- Google Cloud Storage
- S3-compatible endpoints (MinIO, Cloudflare R2)
**Configuration:**
- Cache management for cloud files
- Multi-user system configurations
- Git repository sync
- Named environment variables for credentials and connection URLs
**Deployment patterns:**
- Local dev → Cloud production migration
- Multi-region deployments
- Shared storage with personal instances
**Reference:** `references/setup-deployment.md` - Read this for detailed installation, configuration, storage setup, database management, security best practices, and troubleshooting.
## Safety and Security Defaults
When helping with LaminDB setup or integrations:
- Never display, log, or transmit actual API keys, cloud credentials, database passwords, or full connection strings that include secrets.
- Prefer IAM roles, workload identity, secret managers, or named environment variables such as `LAMIN_DB_URL`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `GOOGLE_APPLICATION_CREDENTIALS`; only check whether a named variable is present, not its value.
- Before saving content from REST APIs, external databases, or user-provided files, validate and sanitize it with an explicit schema or curator.
- For reproducible installs, pin package versions or use a lock file. Floating installs are acceptable only when the user explicitly wants the latest upstream release.
## Common Use Case Workflows
### Use Case 1: Single-Cell RNA-seq Analysis with Ontology Validation
```python
import lamindb as ln
import bionty as bt
import anndata as ad
# Start tracking a notebook/script run
ln.track(params={"analysis": "scRNA-seq QC and annotation"})
For native Nextflow projects, prefer the `nf-lamin` plugin and current `nextflow.config` patterns when available; use inline Python tracking for small or custom pipeline steps.
