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 molfeatgit 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/molfeat)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/molfeat"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/molfeat/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/molfeat"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/molfeat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 226 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
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 | $0.00047 | $0.03286 |
| Opus 5 | $0.00023 | $0.01643 |
| Sonnet 5 | $0.00009 | $0.00657 |
| Haiku 4.5 | $0.00005 | $0.00329 |
Grade B, and why
molfeat scanned grade B with 1 finding 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 8d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
Prefer molfeat's built-in pretrained-model cache when possible. For custom embedding caches, use NumPy arrays instead of pickle (pickle can execute arbitrary code when loading untrusted files): Copies of this mod
8 near-identical copies found in the catalogue:
- opsx-bulk-archive — 86% identical, 459 lines differ
- openspec-bulk-archive-change — 78% identical, 456 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
- openspec-bulk-archive-change — 77% identical, 453 lines differ
How it starts
The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molfeat - Molecular Featurization Hub
Overview
Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.
Version note: Examples target molfeat 0.11.0 (PyPI stable, May 2025). Requires Python 3.9–3.10 (requires-python caps below 3.11). Depends on datamol ≥0.8.0 and PyTorch ≥1.13. Since 0.8.7, prefer datamol Mol objects over raw rdkit.Chem.Mol. Since 0.10.1, fingerprint calculators use RDKit's rdFingerprintGenerator API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically.
When to Use This Skill
This skill should be used when working with:
- Molecular machine learning: Building QSAR/QSPR models, property prediction
- Virtual screening: Ranking compound libraries for biological activity
- Similarity searching: Finding structurally similar molecules
- Chemical space analysis: Clustering, visualization, dimensionality reduction
- Deep learning: Training neural networks on molecular data
- Featurization pipelines: Converting SMILES to ML-ready representations
- Cheminformatics: Any task requiring molecular feature extraction
Installation
Use a Python 3.9 or 3.10 environment (molfeat does not install on 3.11+ as of 0.11.0):
uv pip install "molfeat==0.11.0"
# With all pip-installable optional dependencies
uv pip install "molfeat[all]==0.11.0"
Optional dependency extras (PyPI):
molfeat[dgl]— GNN models (GIN variants); upstream recommendsdgl<=2.0(graphbolt issues in newer DGL)molfeat[graphormer]— Graphormer modelsmolfeat[transformer]— ChemBERTa, ChemGPT, MolT5molfeat[fcd]— FCD descriptorsmolfeat[pyg]— PyTorch Geometric featurizersmolfeat[viz]— NGLView visualization widgets
What ships with it
4 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.
- 8d ago First seen · 366 lines · 47 tokens per session scan B 4ba753eec9f3
molfeat is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 47 tokens to every session and 3,286 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). 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-prefect-dask-nextflow
Design reproducible bioinformatics pipelines with Prefect plus Dask or Nextflow. Use when scaffolding local, distributed, or scheduler-backed workflows.
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…