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 yanjumlinnb-boop/scientific-agent-skills --skill lamindbgit clone --depth 1 https://github.com/yanjumlinnb-boop/scientific-agent-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/yanjumlinnb-boop/scientific-agent-skills/lamindb)<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/lamindb"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-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/yanjumlinnb-boop/scientific-agent-skills/lamindb"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-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 | $0.00056 | $0.03703 |
| Opus 5 | $0.00028 | $0.01852 |
| Sonnet 5 | $0.00011 | $0.00741 |
| Haiku 4.5 | $0.00006 | $0.00370 |
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 12d 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
94% identical to lamindb — 20 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 — 407 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
- 12d ago First seen · 407 lines · 56 tokens per session scan A 1955098baf1f
lamindb is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 3,703 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to lamindb, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
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.
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
defining-cohort-phenotypes
Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build…
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
Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
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…