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 agentmods add skills/synthetic-sciences/openscience/lamindbnpx skills add synthetic-sciences/openscience --skill lamindbgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00135 | $0.03274 |
| Opus 5 | $0.00068 | $0.01637 |
| Sonnet 5 | $0.00027 | $0.00655 |
| Haiku 4.5 | $0.00014 | $0.00327 |
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 2d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- lamindb — 100% identical, 3 lines differ
- lamindb — 98% identical, 3 lines differ
- lamindb — 97% identical, 5 lines differ
- lamindb — 97% identical, 3 lines differ
- alterlab-lamindb — 97% identical, 16 lines differ
- lamindb — 95% identical, 6 lines differ
- lamindb — 88% identical, 81 lines differ
- lamindb — 88% identical, 81 lines differ
How it starts
The opening of the file, as written. The whole thing — 390 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.
- 2d ago First seen · 390 lines · 135 tokens per session scan A 56544c9ad567
lamindb is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed today), licensed Apache-2.0. It adds 135 tokens to every session and 3,274 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
squash-merge
Squash-merge a PR into zeroclaw-labs/zeroclaw master with fully preserved commit history in the squash message body. Use this skill when the user explicitly mentions squash-merging, merging a specific PR number, landing a PR, or 合入 — e.g. "squash-merge #123", "merge PR 456", "land #789", "合入 #123", "/squash-merge…
zeroclaw
Help users operate and interact with their ZeroClaw agent instance — through both the CLI (zeroclaw commands) and the REST/WebSocket gateway API. Use this skill whenever the user wants to: send messages to ZeroClaw, manage memory or cron jobs, check system status, configure channels or providers, hit the gateway API…
github-pr
Open or update a GitHub Pull Request for ZeroClaw. Handles creating new PRs with a fully filled-out template body, and updating existing PRs (title, body sections, labels, comments). Use this skill whenever the user wants to open a PR, create a pull request, update a PR, edit PR description, add labels to a PR, or…
feature-matrix-parity
Update the OpenClaw and Hermes comparison columns of the ZeroClaw feature-and-support matrix. Use this skill when the user wants to refresh, fill, or verify parity data in docs/book/feature-matrix-parity.toml, add a new comparison row or section to the feature matrix, or re-walk the competitor repos for support…
github-issue-triage
Issue triage and lifecycle management agent for ZeroClaw. Use this skill whenever the user wants to: triage open issues, close stale/duplicate/fixed issues, apply labels, run a backlog sweep, enforce the current issue stale policy, or handle a specific issue. Trigger on: 'triage issues', 'issue triage', 'sweep…
meta-paper-write
Use this meta-skill instead of answering directly when the current user asks to draft or produce a new academic/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure/table placeholders, drafting, length checks…