Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill lamindbgit clone --depth 1 https://github.com/K-Dense-AI/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/k-dense-ai/scientific-agent-skills/lamindb)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/lamindb"><img src="https://agentmods.dev/badge/skills/k-dense-ai/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/k-dense-ai/scientific-agent-skills/lamindb"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/lamindb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01953 |
| Opus 5.5 | $0.00023 | $0.00781 |
| Sonnet 5.5 | $0.00012 | $0.00391 |
| Haiku 4.5 | $0.00006 | $0.00195 |
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 26d 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, 474 lines differ
- lamindb — 94% identical, 486 lines differ
- lamindb — 94% identical, 486 lines differ
- lamindb — 94% identical, 486 lines differ
- lamindb — 91% identical, 466 lines differ
- lamindb — 91% identical, 466 lines differ
- lamindb — 91% identical, 466 lines differ
- lamindb — 88% identical, 466 lines differ
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LaminDB
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; registration alone does not establish scientific validity or FAIR compliance.
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 (add_synonym() imports it in this release).
Workflow
- Confirm the intended instance, storage, and write scope with
lamin info. Use a new local development directory for experiments. See setup and deployment. - Define typed
Featurerecords and aSchemafor the biological data. Use registry-backed categorical dtypes for controlled vocabularies;dtype=stronly checks strings. See annotation and validation. - Review the ontology source/version and organism. Standardize only reviewed synonyms and preserve unresolved annotations. See Bionty ontologies.
- In a script or notebook, start
ln.track(), load registered inputs, validate, and save outputs.curator.validate()returnsNoneon success and raisesln.errors.ValidationErroron failure. Repair, then validate again. - 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(). - Query metadata before loading content. Record artifact UIDs and schema/source provenance for reproducibility; a key can resolve to a newer version later. See queries and streaming.
Local worked example
Run in a dedicated environment and an empty project directory:
What ships with it
7 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.
- 26d ago Changed · -276 lines · +2 tokens per session 00f1246a7f59
- 27d ago Changed · +6 lines 386e97ef3093
- 1mo ago First seen · 425 lines · 56 tokens per session scan A 3f80e651f16d
lamindb is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 1,953 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
bio-fasta-database-curator
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bio-foundation-housekeeping
Add LinkML/Pydantic metadata validation, Parquet tables, and a DuckDB catalog to a bioinformatics project. Use when records need schema checks or a queryable catalog.
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
alterlab-tiledbvcf
Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets that are too large for flat VCF, building joint variant…
laravel-eloquent
Use when working with database models — Eloquent ORM, PHP attributes, relationships, queries, observers, or factories in Laravel 13.