Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add crazymsn/academic-skills --skill lamindbgit clone --depth 1 https://github.com/crazymsn/academic-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/crazymsn/academic-skills/lamindb)<a href="https://agentmods.dev/skills/crazymsn/academic-skills/lamindb"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/lamindb/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/crazymsn/academic-skills/lamindb"><img src="https://agentmods.dev/badge/skills/crazymsn/academic-skills/lamindb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00135 | $0.03261 |
| Opus 5 | $0.00068 | $0.01631 |
| Sonnet 5 | $0.00027 | $0.00652 |
| Haiku 4.5 | $0.00014 | $0.00326 |
Grade A, and why
lamindb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to lamindb — 96 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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. Core Concepts and Data Lineage
Core entities:
- Artifacts: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.)
- Records: Experimental entities (samples, perturbations, instruments)
- Runs & Transforms: Computational lineage tracking (what code produced what data)
- Features: Typed metadata fields for annotation and querying
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 387 lines · 135 tokens per session scan A 326e93dfe1e2
lamindb is a skill published in the GitHub repository crazymsn/academic-skills (22 stars, last pushed 3mo ago), licensed MIT. It adds 135 tokens to every session and 3,261 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to lamindb, differing in 96 lines, and is treated as a copy.
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benchling-integration
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
chembl-database
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nvalchemi-data-storage
How to write, read, compose, and load atomic data using nvalchemi's composable Zarr-backed storage pipeline (Writer, Reader, Dataset, MultiDataset, DataLoader). Use when saving simulation outputs or trajectories to disk, converting structures (e.g. ASE / extxyz) into Zarr stores, assembling datasets for training or…