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 esmgit 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/esm)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/esm"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/esm/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/esm"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/esm.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 | $0.00056 | $0.02558 |
| Opus 5.5 | $0.00022 | $0.01023 |
| Sonnet 5.5 | $0.00011 | $0.00512 |
| Haiku 4.5 | $0.00006 | $0.00256 |
Grade A, and why
esm 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.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ESM: protein generation, embeddings, and folding
When to use
Use for Biohub's esm SDK: ESM3 masked multimodal generation, ESMC sequence
representations, and ESMFold2 all-atom prediction. The former Forge platform
migrated to https://biohub.ai, including ESM3; SDK class names still contain
Forge. This skill targets the released esm 3.4.1.post1, verified against
its wheel and current official documentation.
fair-esm is Meta's separate, older ESM2/ESMFold/ESM-IF distribution. Both
packages import as esm; install them in separate environments. The legacy
esm.pretrained.esm2_* interface is not the Biohub ESMC interface. Hugging Face
Transformers' native ESMC implementation is another API: do not interchange its
version requirements or output types with this SDK.
Setup and model choice
uv venv --python 3.12 .venv-esm
uv pip install --python .venv-esm/bin/python "esm==3.4.1.post1"
The release declares Python >=3.12, Torch >=2.11,<2.12 and Transformers
=4.57.6,<5. Python 3.12, Torch 2.11.0 and Transformers 4.57.6 were tested on CPU. Linux x86_64 installs include GPU-specific dependencies; use a supported inference machine and budget disk space. Flash Attention is optional; it is unnecessary for the tiny CPU tests. Do not install this stack into a shared environment containing incompatible Transformers or Torch pins.
| Task | Local model/API | Hosted client/model |
|---|---|---|
| ESM3 sequence/structure/function generation | ESM3.from_pretrained("esm3-sm-open-v1") |
client("esm3-medium-2024-08"); small/large IDs in the ESM3 reference |
| ESMC embeddings | EsmcForMaskedLM and EsmcTokenizer; biohub/ESMC-300M, biohub/ESMC-600M, biohub/ESMC-6B |
esmc_client("esmc-600m-2024-12"); 300M/6B also documented |
| ESMFold2 all-atom structures | EsmFold2Model, ESMFold2InputBuilder; biohub/ESMFold2 |
esmfold2_client("esmfold2-fast-2026-05") |
ESMC 6B weights are now available locally. Current Biohub model cards identify
MIT licensing, with ESMC cards also linking third-party notices; review the
exact selected artifact's card and access requirements.
The older ESMC class remains as a deprecated compatibility wrapper. Local
model sizes, hosted availability and account quotas are different constraints;
a larger model does not guarantee better performance on a particular assay.
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 · -140 lines · +17 tokens per session 49da3c6f5c81
- 27d ago Changed · +4 lines 58c52b114ac0
- 1mo ago Changed · +16 lines d71c806231ff
- 1mo ago First seen · 335 lines · 39 tokens per session scan A 439047e87cf7
esm is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 2,558 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 2026-08-30.
Other skills, from other repositories
bio-prefect-dask-nextflow
Choose and scaffold Prefect+Dask or Nextflow pipelines. Use when designing a local, distributed, or Slurm-backed bioinformatics workflow.
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…
rdkit-qsar-pharmacophore
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
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…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…