domains / science / biological-data-curation / lamindb

lamindb

Skill card

combined

The K-Dense-AI copy is a version-pinned, executable procedure (LaminDB 2.10.0) that explicitly warns against APIs present in the other copies (e.g., it states `curator.cat.add_ontology()` is absent in 2.10.0, while the other copies list it as a key workflow). The other three copies (Vibe-Skills, aipoch, OpenClaw) are nearly identical high-level overviews that lack the specific version constraints, failure patterns, and worked examples found in the K-Dense-AI copy. Because the K-Dense-AI copy contains critical version-specific constraints that contradict the general advice in the other copies, crowning one would hide the version-specific reality, while the others provide a broader conceptual overview. They must be combined to provide both the conceptual framework and the version-specific execution details.

Repositories

Other copies

Combined skill

---
name: lamindb
description: Manages biological datasets and models with LaminDB, including artifact registration, lineage tracking, schema validation, Bionty ontology annotation, query/search, collections, branches, storage, and workflow integrations. Use for reproducible biological data curation or a LaminDB lakehouse.
---

# 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, and ELN capabilities through a single Python API.

## When to Use
Use for registering biological datasets, curating DataFrame/AnnData metadata, querying annotated artifacts, and tracking scientific scripts or workflows. LaminDB stores metadata in SQLite/PostgreSQL and files in configured storage.

## Version Constraints
This skill targets **LaminDB 2.10.0 and Bionty 2.5.0**. The local examples were executed on Python 3.12, pandas 3.0.6, and AnnData 0.13.2. The LaminDB release caps AnnData at 0.13.2; preserve its dependency constraints. Install IPython for registry synonym helpers.

## Workflow
1. Confirm the intended instance, storage, and write scope with `lamin info`. Use a new local development directory for experiments.
2. Define typed `Feature` records and a `Schema` for the biological data. Use registry-backed categorical dtypes for controlled vocabularies.
3. Review the ontology source/version and organism. Standardize only reviewed synonyms and preserve unresolved annotations.
4. In a script or notebook, start `ln.track()`, load registered inputs, validate, and save outputs. `curator.validate()` returns `None` on success and raises `ln.errors.ValidationError` on failure.
5. Save through the curator or a schema-aware artifact constructor. Check the stored schema and round-trip content; finish a successful run with `ln.finish()`.
6. Query metadata before loading content. Record artifact UIDs and schema/source provenance for reproducibility.

## Local Worked Example
Run in a dedicated environment and an empty project directory:

```bash
uv venv --python 3.12
uv pip install 'lamindb==2.10.0' 'bionty==2.5.0' 'ipython==9.17.1'
source .venv/bin/activate
lamin init --storage ./storage --name biology-demo --modules bionty
```

Save the following as `curate_qc.py`, then run `python curate_qc.py`:

```python
import pandas as pd
import lamindb as ln

ln.track(params={"analysis": "QC metadata curation"})
count = ln.Feature(name="gene_count", dtype=int).save()
condition = ln.Feature(name="condition", dtype=str).save()
schema = ln.Schema(
    name="qc_metadata",
    features=[count, condition],
    maximal_set=True,  # reject extra columns
).save()
df = pd.DataFrame({
    "gene_count": [120, 130],
    "condition": ["control", "treated"],
})
curator = ln.curators.DataFrameCurator(df, schema)
curator.validate()  # raises on invalid data; do not use as an if condition
artifact = curator.save_artifact(key="experiments/qc.parquet")
assert artifact.schema == schema
pd.testing.assert_frame_equal(artifact.load(), df)
ln.finish()
```

## Core Objects and Contracts
| Object | Purpose and important constraint |
| --- | --- |
| `Artifact` | A file/folder or serialized dataset; `cache()` gets a local path, `load()` materializes content, `open()` returns a format-specific accessor. |
| `Feature` | A typed metadata field; save its definition before `artifact.features.set_values(...)`. |
| `Schema` | Validation rules and feature membership; `maximal_set=True` rejects extras. `flexible=False` alone does not. |
| `Record`, `ULabel` | Experimental entities and simple labels. A custom term is not an ontology assertion. |
| `Run`, `Transform` | An execution and its code definition. Inputs are `run.input_artifacts`; outputs are `run.output_artifacts`. |
| `Collection` | A versioned group of artifacts; iterate `collection.artifacts.all()`. |
| `Project`, `Branch`, `Space` | Grouping, change organization, and access scope; branches do not replace permissions. |

Use `@ln.flow()` for a workflow entry point and `@ln.step()` within it. Lineage captures tracked accesses, not arbitrary reads outside LaminDB.

## Failure Patterns to Avoid
- `Artifact.backed()`, `delete_cache()`, and `is_cached()` are absent in 2.10.0. Use the supported `open()`/`cache()` contracts and cache settings.
- A Parquet `open()` result is a PyArrow dataset, not a byte stream. An AnnData accessor is a context manager; materialize a slice with `.to_memory()`.
- There is no `Artifact.is_valid` query field. Query the exact `schema` used and retain validation evidence.
- `curator.cat.add_ontology()` and `inspect_standardize()` are absent in 2.10.0. Define the feature's Bionty dtype, use source-backed records, and use `cat.standardize()`. For AnnData use `curator.slots["obs"].cat`, not `curator.cat`.
- Bionty `from_values()` can omit unresolved values and return unsaved records. Check all values explicitly and save records before linking them.

## Conflicts
- **K-Dense-AI/scientific-agent-skills** states that `curator.cat.add_ontology()` is absent in LaminDB 2.10.0, while **foryourhealth111-pixel/Vibe-Skills**, **aipoch/medical-research-skills**, and **FreedomIntelligence/OpenClaw-Medical-Skills** list `.cat.add_ontology()` as a key workflow. The version-pinned constraint in K-Dense-AI is the authoritative step for the 2.10.0 environment.