Scientific Agent Skills

A comprehensive collection of 158 ready-to-use scientific and research skills (covering cancer genomics, individual-level 1000 Genomes queries, hosted regulatory-sequence prediction, live pathogen-variant surveillance, analytical method validation, PK/PD modelling and dose selection, full-text biomedical and regulatory literature retrieval, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open Agent Skills standard, created by K-Dense. Works with Cursor, Claude Code, Codex, Google Antigravity, and more. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.

Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.

These skills enable your AI agent to seamlessly work with specialized scientific libraries, databases, and tools across multiple scientific domains. While the agent can use any Python package or API on its own, these explicitly defined skills provide curated documentation and examples that make it significantly stronger and more reliable for the workflows below:

• 🧬 Bioinformatics & Genomics – Sequence analysis, single-cell RNA-seq, gene regulatory networks, variant annotation, phylogenetic analysis
• 🧪 Cheminformatics & Drug Discovery – Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization
• 🔬 Proteomics & Mass Spectrometry – LC-MS/MS processing, peptide identification, spectral matching, protein quantification
• 🏥 Clinical Research & Evidence Workflows – Clinical trials, pharmacogenomics, variant evidence review, pharmacokinetic/pharmacodynamic modelling and dose-regimen evaluation, aggregate decision-support evaluation, source-bound draft report structures, and formatting of clinician-authored treatment decisions
• 🧠 Healthcare AI & Biosignal Research – EHR and model research, physiological signal analysis, and retrospective validation—not patient-specific diagnosis, treatment, alarms, or deployment decisions
• 🖼️ Medical Imaging & Digital Pathology – Privacy-aware DICOM processing and research-only whole-slide image analysis, computational pathology, and radiology data workflows
• 🤖 Machine Learning & AI – Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods
• 🔮 Materials Science & Chemistry – Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry
• 🌌 Physics & Astronomy – Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations
• ⚙️ Engineering & Simulation – Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization
• 📊 Data Analysis & Visualization – Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA
• 🌍 Geospatial Science & Remote Sensing – Satellite imagery processing, GIS analysis, spatial statistics, terrain analysis, machine learning for Earth observation
• 🧪 Laboratory Automation – Liquid handling protocols, lab equipment control, workflow automation, LIMS integration
• 📚 Scientific Communication – Evidence-traceable writing, confidential authorized peer review, literature synthesis, document processing, macro-free PPTX posters, slides, schematics, and citation management
• 🔬 Multi-omics & Systems Biology – Multi-modal data integration, pathway analysis, network biology, systems-level insights
• 🧬 Protein Engineering & Design – Protein language models, structure prediction, sequence design, function annotation
• 🧰 Agent Platforms & Infrastructure – Build on Pi with SDK, RPC, extensions, custom providers/models, packages, TUI components, and session tooling
• 🎓 Research Methodology – Evidence-bounded candidate hypotheses, scientific brainstorming, critical thinking, grant writing, and qualitative low-stakes evaluation of scholarly works
• ⚖️ Regulatory & Standards – Draft evidence-preparation artifacts for ISO management-system and laboratory standards, plus analytical method validation, verification, and transfer under ICH/USP/CLSI frameworks—prepared for qualified review, never a certification, accreditation, or method-release decision

Transform your AI coding agent into an 'AI Scientist' on your desktop!

🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills video for a quick walkthrough.

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📦 What's Included

This repository provides 158 scientific and research skills organized into the following categories:

• 100+ Scientific & Financial Databases – A unified database-lookup skill provides deterministic, provenance-rich access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, U.S. Treasury Fiscal Data, Hugging Science, OneKGPd, and Genomic Intelligence. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage.
• 70+ Optimized Python Package Skills – Explicitly defined, version-aware workflows for RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, PathML, pydicom, NeuroKit2, PufferLib, QuTiP, GeoPandas, pymatgen, BioPython, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), and others. The agent can still use any Python package; these skills provide stronger, safer guidance for the packages listed.
• 9 Scientific Integration Skills – Explicitly defined skills for Benchling, DNAnexus, LatchBio, OMERO, Protocols.io, Open Notebook, Ginkgo Cloud Lab, LabArchives, and Opentrons. Again, the agent is not limited to these — any API or platform reachable from Python is fair game; these skills are the optimized, pre-documented paths.
• 30+ Analysis & Communication Tools – Literature review, evidence-traceable scientific writing, confidential peer review, document processing, Paperclip (full-text papers, FDA/PMDA/EMA filings, and trial registries with line-pinned citations), Paperzilla, Exa Search, macro-free PPTX posters, slides, schematics, infographics, Mermaid diagrams, and more.
• 10+ Research & Clinical Tools – Evidence-bounded hypothesis generation, grant writing, aggregate clinical decision-support research, clinician-authored treatment-plan formatting, PK/PD modelling and simulation (NCA, population PK, exposure-response, bioequivalence, first-in-human dose), BIDS, ISO standards-readiness evidence preparation (ISO 13485, ISO 14971, ISO/IEC 17025, ISO 15189), analytical method validation and transfer (ICH Q2(R2)/Q14, ICH M10, USP, CLSI EP), scenario analysis, and workflow-derived skill drafting with Autoskill.

Each skill includes:

• ✅ Comprehensive documentation (SKILL.md)
• ✅ Practical code examples
• ✅ Use cases and best practices
• ✅ Integration guides
• ✅ Reference materials
• ✅ A test suite for every skill that ships scripts/

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🚀 Why Use This?

⚡ Accelerate Your Research

• Skip API documentation research and integration setup
• Reviewed starting points – Tested examples with explicit validation, provenance, and safety boundaries; verify them in the target environment
• Multi-step workflows – Execute complex pipelines with a single prompt

🎯 Comprehensive Coverage

• 158 Skills – Extensive coverage across all major scientific domains
• 100+ Databases – Unified access to 78+ databases via database-lookup, plus dedicated data access skills and multi-database packages like BioServices, BioPython, and gget
• 70+ Optimized Python Package Skills – Current, version-scoped guidance for packages including RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, pydicom, PufferLib, QuTiP, GeoPandas, pymatgen, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, and TimesFM (the agent can use any Python package; these are the pre-documented paths)

🔧 Easy Integration

• Configured Discovery – Compatible hosts can find and use relevant skills from their configured skill paths
• Well documented – Each skill includes examples, use cases, and best practices

🌟 Maintained & Supported

• Regular Updates – Continuously maintained and expanded by K-Dense team
• Community driven – Open source with active community contributions
• Enterprise ready – Commercial support available for advanced needs

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⚙️ Prerequisites

• Python: 3.13+ for repository tooling; individual skill dependencies may support broader Python ranges
• uv: Python package manager (required for installing skill dependencies)
• Client: Any agent that supports the Agent Skills standard (Cursor, Claude Code, Gemini CLI, Codex, Google Antigravity, etc.)
• System: macOS, Linux, or Windows with WSL2
• Dependencies: Automatically handled by individual skills (check SKILL.md files for specific requirements)

Installing uv

The skills use uv as the package manager for installing Python dependencies. Install it using the instructions for your operating system:

macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Alternative (via pip):

pip install uv

After installation, verify it works by running:

uv --version

For more installation options and details, visit the official uv documentation.

NemoClaw note: NemoClaw runs agents inside NVIDIA OpenShell with default-deny outbound networking. Skills are discovered and loaded normally, but any skill that needs the network — package installs via uv, or API calls (Exa, Parallel, Benchling, NCBI, Materials Project, …) — only works once the operator pre-approves the relevant domains in the OpenShell TUI.

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⚠️ Security Disclaimer

Skills can execute code and influence your coding agent's behavior. Review what you install.

Agent Skills are powerful — they can instruct your AI agent to run arbitrary code, install packages, make network requests, and modify files on your system. A malicious or poorly written skill has the potential to steer your coding agent into harmful behavior.

We take security seriously. All contributions go through a review process, and we run LLM-based security scans (via Cisco AI Defense Skill Scanner) on every skill in this repository. However, as a small team with a growing number of community contributions, we cannot guarantee that every skill has been exhaustively reviewed for all possible risks.

It is ultimately your responsibility to review the skills you install and decide which ones to trust.

We recommend the following:

• Do not install everything at once. Only install the skills you actually need for your work.
• Read the SKILL.md before installing. Each skill's documentation describes what it does, what packages it uses, and what external services it connects to. If something looks suspicious, don't install it.
• Check the contribution history. Skills authored by K-Dense (K-Dense-AI) have been through our internal review process. Community-contributed skills have been reviewed to the best of our ability, but with limited resources.
• Report anything suspicious. If you find a skill that looks malicious or behaves unexpectedly, please open an issue immediately so we can investigate.

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💡 Quick Examples

🧪 Drug Discovery Pipeline

Prompt:

Query ChEMBL for EGFR inhibitors (IC50 < 50nM), analyze structure-activity relationships with RDKit, generate improved analogs with datamol, perform virtual screening with DiffDock against AlphaFold EGFR structure, search PubMed for resistance mechanisms, check COSMIC for mutations, and create visualizations and a comprehensive report.

Skills Used: database-lookup, rdkit, datamol, diffdock, paper-lookup, scientific-visualization

🔬 Single-Cell RNA-seq Analysis

Prompt:

Load 10X dataset with Scanpy, perform QC and doublet removal, integrate with Cellxgene Census data, identify cell types using NCBI Gene markers, run differential expression with PyDESeq2, infer gene regulatory networks with Arboreto, enrich pathways via Reactome/KEGG, and identify therapeutic targets with Open Targets.

Skills Used: scanpy, cellxgene-census, database-lookup, pydeseq2, arboreto

🧬 Multi-Omics Biomarker Discovery

Prompt:

Analyze RNA-seq with PyDESeq2, process mass spec with pyOpenMS, integrate metabolites from HMDB/Metabolomics Workbench, map proteins to pathways (UniProt/KEGG), find interactions via STRING, correlate omics layers with statsmodels, build predictive model with scikit-learn, and search ClinicalTrials.gov for relevant trials.

Skills Used: pydeseq2, pyopenms, database-lookup, statsmodels, scikit-learn

🎯 Virtual Screening Campaign

Prompt:

Retrieve AlphaFold structures, identify interaction interface with BioPython, search ZINC for allosteric candidates (MW 300-500, logP 2-4), filter with RDKit, dock with DiffDock, rank with DeepChem, check PubChem suppliers, search USPTO patents, and optimize leads with MedChem/molfeat.

Skills Used: database-lookup, biopython, rdkit, diffdock, deepchem, medchem, molfeat

🏥 Research Variant Evidence Review

Prompt:

Work only with authorized synthetic or de-identified data. Parse the VCF with pysam, annotate variants with Ensembl VEP, retrieve ClinVar/COSMIC/NCBI Gene/UniProt evidence, and verify literature sources. Build an evidence-traceable research summary with scientific-writing. If clinical-reports is used, create only a visibly marked draft structure from a verified source-fact manifest for qualified review; do not diagnose, assess individual risk, recommend treatment, or determine trial eligibility.

Skills Used: pysam, database-lookup, paper-lookup, scientific-writing, clinical-reports

🌐 Systems Biology Network Analysis

Prompt:

Query NCBI Gene for annotations, retrieve sequences from UniProt, identify interactions via STRING, map to Reactome/KEGG pathways, analyze topology with Torch Geometric, reconstruct GRNs with Arboreto, assess druggability with Open Targets, model with PyMC, visualize networks, and search GEO for similar patterns.

Skills Used: database-lookup, torch-geometric, arboreto, pymc, networkx, scientific-visualization

📖 Want more examples? Check out docs/examples.md for comprehensive workflow examples and detailed use cases across all scientific domains.

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🔬 Use Cases

🧪 Drug Discovery & Medicinal Chemistry

• Virtual Screening: Screen millions of compounds from PubChem/ZINC against protein targets
• Lead Optimization: Analyze structure-activity relationships with RDKit, generate analogs with datamol
• ADMET Prediction: Predict absorption, distribution, metabolism, excretion, and toxicity with DeepChem
• Molecular Docking: Predict binding poses with DiffDock and rescore poses with affinity-oriented tools
• Bioactivity Mining: Query ChEMBL for known inhibitors and analyze SAR patterns

🧬 Bioinformatics & Genomics

• Sequence Analysis: Process DNA/RNA/protein sequences with BioPython and pysam
• Single-Cell Analysis: Analyze 10X Genomics data with Scanpy, identify cell types, infer GRNs with Arboreto
• Variant Annotation: Annotate research VCF files with Ensembl VEP and retrieve ClinVar evidence for qualified interpretation
• Variant Database Management: Build scalable VCF databases with TileDB-VCF for incremental sample addition, efficient population-scale queries, and compressed storage of genomic variant data
• Population Genomics: Query variants, cohort sample IDs, and relatedness in the 3,202-person GRCh38 1000 Genomes cohort with OneKGPd
• Regulatory Sequence Models: Run hosted Genomic Intelligence promoter, splice, enhancer, chromatin, expression, and gene-annotation predictions for research—not clinical or diagnostic decisions
• Pathogen Surveillance: Track which viral lineages are circulating now and how fast they are growing (SARS-CoV-2, influenza including H5N1, RSV, mpox, measles, dengue) through the GenSpectrum LAPIS API, with reporting lag measured rather than assumed
• Gene Discovery: Query NCBI Gene, UniProt, and Ensembl for comprehensive gene information
• Network Analysis: Identify protein-protein interactions via STRING, map to pathways (KEGG, Reactome)

🏥 Clinical Research & Evidence Workflows

• Clinical Trials: Analyze aggregate trial landscapes and protocol criteria without deciding individual eligibility
• Variant Evidence Review: Annotate authorized research data with ClinVar, COSMIC, and ClinPGx; qualified professionals retain interpretation responsibility
• Drug Safety Research: Query FDA databases for aggregate adverse-event, interaction, and recall evidence
• Clinical Pharmacology: Derive exposure metrics from concentration-time data, fit compartmental and population PK models, relate exposure to effect, and evaluate dosing regimens, bioequivalence, and first-in-human dose
• Full-Text Evidence Retrieval: Search and read papers, regulatory filings, and trial records end to end with Paperclip, returning citations pinned to line numbers rather than to abstracts
• Decision-Support Evaluation: Prepare synthetic or aggregate evaluation, evidence-profile, privacy, and governance artifacts—not live clinical decisions
• Clinician-Authored Documentation: Structure verified source-bound report drafts and format treatment decisions already made by authorized licensed professionals

🔬 Multi-Omics & Systems Biology

• Multi-Omics Integration: Combine RNA-seq, proteomics, and metabolomics data
• Pathway Analysis: Enrich differentially expressed genes in KEGG/Reactome pathways
• Network Biology: Reconstruct gene regulatory networks, identify hub genes
• Biomarker Discovery: Integrate multi-omics layers to predict patient outcomes

📊 Data Analysis & Visualization

• Statistical Analysis: Perform hypothesis testing, power analysis, and experimental design
• Publication Figures: Create publication-quality visualizations with matplotlib and seaborn
• Network Visualization: Visualize biological networks with NetworkX
• Report Generation: Produce evidence-traceable research reports with Scientific Writing and document tools; Clinical Reports outputs remain visibly marked drafts built only from verified synthetic, de-identified, or aggregate source facts

🧪 Laboratory Automation

• Protocol Design: Author and simulate Opentrons or PyLabRobot protocols before trained-operator review
• LIMS/ELN Integration: Prepare scoped Benchling and LabArchives operations with explicit authorization for remote writes
• Workflow Automation: Validate and simulate multi-step laboratory workflows offline; physical execution stays behind equipment-specific operator safety gates

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📚 Available Skills

This repository contains 158 scientific and research skills organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.

Note: The Python package and integration skills listed below are explicitly defined skills — curated with documentation, examples, and best practices for stronger, more reliable performance. They are not a ceiling: the agent can install and use any Python package or call any API, even without a dedicated skill. The skills listed simply make common workflows faster and more dependable.

🧬 Bioinformatics & Genomics (26 skills)

• RNA-seq pipelines: Bulk RNA-seq (end-to-end FASTQ -> counts -> DE -> enrichment orchestrator)
• Sequence analysis: BioPython, pysam, scikit-bio, BioServices
• Single-cell analysis: Scanpy, AnnData, scvi-tools, scVelo (RNA velocity), Arboreto, Cellxgene Census
• Genomic tools: gget, current geniml/Gtars interval workflows, deepTools, FlowIO, Polars-Bio, Zarr, TileDB-VCF
• Coordinate hygiene: Genomic Coordinates (convert intervals across BED/GFF/GTF/VCF/SAM/WIG conventions, normalise variant representations, and catch 0-based vs 1-based and assembly/contig-naming mismatches before they corrupt an analysis)
• Population and sequence intelligence: OneKGPd (individual-level 1000 Genomes cohort queries) and Genomic Intelligence (hosted regulatory/gene-expression predictions; research only)
• Differential expression: PyDESeq2
• Functional enrichment: Pathway Enrichment (ORA, GSEA/preranked, ssGSEA via gseapy + g:Profiler; GO, KEGG, Reactome, WikiPathways, MSigDB)
• Phylogenetics: ETE Toolkit, Phylogenetics (MAFFT, IQ-TREE 2, FastTree)

🧪 Cheminformatics & Drug Discovery (10 skills)

• Molecular manipulation: RDKit, Datamol, Molfeat
• Deep learning: DeepChem, TorchDrug
• Docking & screening: DiffDock
• Molecular dynamics: OpenMM + MDAnalysis (MD simulation & trajectory analysis)
• Cloud quantum chemistry: Rowan (pKa, docking, cofolding)
• Drug-likeness: MedChem
• Benchmarks: PyTDC 1.1.15 on its verified CPython 3.11 compatibility stack

🔬 Proteomics & Mass Spectrometry (2 skills)

• Spectral processing: matchms, pyOpenMS

🏥 Clinical Research & Evidence Workflows (8 skills)

• Clinical databases: via Database Lookup (ClinicalTrials.gov, ClinVar, ClinPGx, COSMIC, FDA, cBioPortal, Monarch, and more)
• Clinical pharmacology: PK/PD Modeling (non-compartmental analysis, compartmental and population PK, exposure-response and Emax, TMDD, PBPK orientation, bioequivalence including RSABE/ABEL, allometric scaling and first-in-human dose, DDI prediction under ICH M12, concentration-QTc, and Bayesian therapeutic drug monitoring — stdlib + numpy/scipy, no proprietary estimation software invoked)
• Cancer genomics: DepMap (cancer dependency scores, drug sensitivity)
• Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
• Healthcare AI research: PyHealth
• Decision-support research: local, aggregate or synthetic Clinical Decision Support evaluation and governance artifacts only
• Clinical documentation: source-bound Clinical Reports drafts and formatting of verified clinician-authored decisions with Treatment Plans; neither skill diagnoses or recommends care

🖼️ Medical Imaging & Digital Pathology (3 skills)

• DICOM processing: pydicom 3.0.2 with privacy-first local preflight and no diagnostic or de-identification-compliance claims
• Whole slide imaging: histolab and research-only PathML 3.0.5

🧠 Neuroscience & Electrophysiology (3 skills)

• Data standards: BIDS (Brain Imaging Data Structure for neuroscience and biomedical datasets)
• Neural recordings: Neuropixels-Analysis (extracellular spikes, silicon probes, spike sorting)
• Physiological signals: NeuroKit2 0.2.13 for reproducible research workflows—not diagnosis, monitoring decisions, or medical-device validation

🤖 Machine Learning & AI (14 core skills)

• Deep learning: PyTorch Lightning, Transformers, Stable Baselines3, and version-separated PufferLib 3.0/4.0 workflows
• Classical ML: scikit-learn, scikit-survival 0.28, and SHAP
• Time series: aeon, TimesFM (Google's zero-shot foundation model for univariate forecasting)
• Bayesian methods: PyMC
• Optimization: PyMOO
• Graph ML: Torch Geometric
• Dimensionality reduction: UMAP-learn
• Statistical modeling: statsmodels

🔮 Materials Science, Chemistry & Physics (7 skills)

• Materials: current split pymatgen wrapper/core plus explicitly bounded Materials Project queries
• Metabolic modeling: COBRApy
• Astronomy: Astropy
• Quantum computing: Cirq, PennyLane, Qiskit, QuTiP 5.3

⚙️ Engineering & Simulation (5 skills)

• Numerical computing: proprietary MATLAB R2026a and distinct GNU Octave 11.3 planning/review workflows
• Computational fluid dynamics: bounded FluidSim 0.9 simulations with numerical-validity and HPC checks
• Experimental flow measurement: OpenPIV (velocity fields from PIV image pairs, interrogation-window cross-correlation, spurious-vector validation, vorticity/strain-rate/turbulence statistics)
• Discrete-event simulation: SimPy 4.1.2 with replication, warm-up, and output-analysis guidance
• Symbolic math: SymPy

📊 Data Analysis & Visualization (22 skills)

• Visualization: Matplotlib, Seaborn, Scientific Visualization
• Geospatial analysis: GeoPandas 1.1.4 and GeoMaster (remote sensing, GIS, satellite imagery, spatial ML, 500+ examples)
• Data processing: Dask, Polars, Vaex
• Network analysis: NetworkX
• Document processing: LiteParse (local PDF/document parsing with bounding boxes and OCR), MarkItDown, PDF, DOCX, PPTX, and XLSX
• Infographics: Infographics (AI-powered professional infographic creation)
• Diagrams: Markdown & Mermaid Writing (text-based diagrams as default documentation standard)
• Exploratory data analysis: bounded local EDA for explicitly supported formats, with unknown formats failing closed
• Statistical analysis: Statistical Analysis workflows
• Units and measurement uncertainty: Uncertainty & Units (pint dimensional checking, GUM uncertainty budgets, Type A/B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, CODATA constants)
• Experimental design: Experimental Design (randomization, blocking, factorial/fractional-factorial DOE, crossover, cluster, sequential designs; pyDOE3)
• Statistical power: Statistical Power (sample-size & power for t-tests, ANOVA, proportions, correlation, regression — closed-form plus simulation-based for GLMs, mixed models, and cluster designs)

🧪 Laboratory Automation (6 skills)

• Liquid handling: offline-first PyLabRobot planning/simulation and Opentrons authoring, with physical execution behind explicit operator safety gates
• Cloud lab: Ginkgo Cloud Lab (protein expression & purification across cell-free/E. coli/Pichia, IVT RNA synthesis, thermal shift and Echo-MS assays, SPR onboarding, fluorescent pixel art via autonomous RAC infrastructure)
• Protocol management: bounded protocols.io reads across documented v3/v4 endpoints and non-executing write plans
• LIMS/ELN integration: Benchling and the separate LabArchives legacy ELN and Inventory v1 APIs

🔬 Multi-omics & Systems Biology (3 skills)

• Pathway analysis: via Database Lookup (KEGG, Reactome, STRING) and PrimeKG
• Data management: LaminDB

🧬 Protein Engineering & Design (4 skills)

• Protein language models: ESM
• Glycoengineering: Glycoengineering (N/O-glycosylation prediction, therapeutic antibody optimization)
• Cloud laboratory platform: Adaptyv (automated protein testing and validation)
• Cloud structure & design platform: Tamarind (managed-GPU access to AlphaFold, Boltz, Chai, ESMFold, RFdiffusion, ProteinMPNN, BoltzGen, antibody/nanobody design, DiffDock/Vina docking, binding affinity, and MSA generation via REST API or MCP)

📚 Scientific Communication (27 skills)

• Literature: Paper Lookup (PubMed, PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall), Literature Review, Paperzilla
• Full-text corpus access: Paperclip (read-only virtual filesystem over ~11M full-text papers, 217K+ FDA/PMDA/EMA regulatory documents, clinical trial registries, and UniProt/PDB/ChEMBL entries — source-scoped semantic search, corpus-wide grep, SQL metadata queries, map/reduce reading across many papers, figure vision analysis, and line-pinned citations)
• Advanced paper search: BGPT Paper Search (25+ structured fields per paper — methods, results, sample sizes, quality scores — from full text, not just abstracts)
• Web intelligence: Parallel Web (web search, URL/PDF extraction, deep research, structured enrichment, entity discovery, and recurring monitoring), Exa Search, and Research Lookup
• Research notebooks: Open Notebook (self-hosted NotebookLM alternative — PDFs, videos, audio, web pages; 16+ AI providers; multi-speaker podcast generation)
• Writing: evidence-traceable Scientific Writing and local, confidential, authorized Peer Review
• Document processing: LiteParse, PDF, DOCX, PPTX, XLSX, and MarkItDown
• Publishing and paper workflows: Venue Templates
• Presentations: Scientific Slides, LaTeX Posters, and macro-free PPTX Posters generated from author-approved local manifests
• Diagrams: Scientific Schematics, Markdown & Mermaid Writing
• Infographics: Infographics (10 types, 8 styles, colorblind-safe palettes)
• Citations: Citation Management, pyzotero
• Illustration: Generate Image (AI image generation with FLUX.2 Pro and Gemini 3.1 Flash Image / Nano Banana 2)

🔬 Scientific Databases & Data Access (10 skills → 100+ databases total)

• Unified access: Database Lookup (78 databases spanning chemistry, genomics, clinical, pathways, patents, economics, and more — PubChem, ChEMBL, UniProt, PDB, AlphaFold, KEGG, Reactome, STRING, ClinVar, COSMIC, ClinicalTrials.gov, FDA, FRED, USPTO, SEC EDGAR, and dozens more — with auditable filters and provenance)
• Cancer genomics: DepMap (cancer cell line dependencies, drug sensitivity, gene effect profiles)
• Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
• Knowledge graph: PrimeKG (precision medicine knowledge graph — genes, drugs, diseases, phenotypes)
• Fiscal data: U.S. Treasury Fiscal Data (national debt, Treasury statements, auctions, exchange rates)
• Scientific ML resource catalog: Hugging Science (curated index of datasets, models, blog posts, and interactive Spaces across 17 scientific domains — astronomy, biology, chemistry, climate, genomics, materials science, medicine, physics, scientific reasoning, and more — with usage patterns for datasets, transformers, and gradio_client)
• Individual-level population genomics: OneKGPd (3,202-person high-coverage 1000 Genomes cohort queries)
• Hosted regulatory genomics: Genomic Intelligence (promoter, splice, enhancer, chromatin, expression, and gene-annotation predictions for research use)
• Ontology identifiers: Ontology Term Resolution (resolve free-text tissue, cell-type, disease, phenotype, assay, chemical, organism, and developmental-stage labels to term IDs and validate CURIEs against EBI OLS4, for GEO/ENA/BioSamples/CELLxGENE/HCA/ISA-Tab metadata)
• Live pathogen surveillance: Pathogen Variant Surveillance (which viral lineages are circulating now, how fast they are growing, and what mutations they carry — SARS-CoV-2, influenza including H5N1, RSV, mpox, measles, dengue and more through the GenSpectrum LAPIS API, with lineage names resolved against the live pango-designation nomenclature and reporting lag measured rather than assumed)

🔧 Infrastructure & Platforms (11 skills)

• Cloud compute: Modal
• GPU acceleration: Optimize for GPU (CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, RAFT)
• Genomics platforms: DNAnexus, LatchBio
• Workflow engines: Nextflow (build/run/debug Nextflow & nf-core pipelines — DSL2 modules, executors/containers, HPC/cloud scaling) and pacsomatic (operator toolkit for the nf-core/pacsomatic tumor-normal somatic variant-calling workflow)
• Microscopy: OMERO
• Automation: Opentrons
• Resource detection: Get Available Resources on request or before a clearly resource-sensitive local workload; redacted and without stress tests
• Workflow mining: Autoskill (local screenpipe-based repeated workflow detection and skill drafting)
• Agent platform development: Pi Agent (using Pi as a terminal coding harness and building on it with SDK, RPC/JSONL, extensions, custom providers/models, packages, TUI components, and session tooling)

🎓 Research Methodology & Planning (13 skills)

• Ideation: evidence-aware Scientific Brainstorming and non-scoring Hypothesis Generation that keeps hypotheses labeled as candidates
• Text-dataset hypothesis software: HypoGeniC/HypoRefine produces candidate textual patterns and task-prediction statistics, not validated scientific hypotheses
• Autonomous optimization: Arbor (Hypothesis Tree Refinement — iteratively improve a code/model/agent-harness/data artifact against a dev evaluator while a held-out test gate guards against overfitting)
• Critical analysis: Scientific Critical Thinking and qualitative, low-stakes Scholar Evaluation of works—never ranking people or supporting consequential decisions
• Scenario analysis: What-If Oracle (4–6 branch possibility exploration, contingency planning, decision stress-testing)
• Multi-perspective deliberation: Consciousness Council (diverse expert viewpoints, devil's advocate analysis)
• Cognitive profiling: DHDNA Profiler (extract thinking patterns and cognitive signatures from any text)
• Funding: Research Grants
• Discovery: Research Lookup, Paper Lookup (10 academic databases)
• Market analysis: evidence-traceable Market Research Reports with assumption-led sizing and forecast sensitivity

⚖️ Regulatory & Standards (2 skills)

• Standards readiness: draft evidence-preparation artifacts for ISO 13485 (medical device QMS), ISO 14971 (device risk management), ISO/IEC 17025 (testing and calibration laboratories), and ISO 15189 (medical laboratories), with per-standard process domains selected by a --standard profile
• Analytical method validation: plan, evaluate, and document validation, verification, and transfer of analytical procedures (HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, ligand-binding and cell-based assays) under whichever framework governs — ICH Q2(R2)/Q14 and ICH M10 encoded from their openly licensed text, with USP <1220>/<1225>/<1226>, the CLSI EP series, and ISO/IEC 17025 cited by designation and scope only; stdlib-only statistics, no network access
• Assurance-lane separation: keeps ISO certification, laboratory accreditation, FDA QMSR inspection, CLIA certification, MDSAP, and EU MDR/IVDR evidence boundaries distinct—laboratories are accredited rather than certified, and ISO 15189 accreditation does not satisfy CLIA
• Never a compliance, audit, assessment, certification, accreditation, or method-release decision; qualified RA/QA, legal, laboratory-director, assessor, and certification-body review is required

📖 For complete details on all skills, see docs/skills.md

💡 Looking for practical examples? Check out docs/examples.md for comprehensive workflow examples across all scientific domains.

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📝 From the Blog

Deep dives, benchmarks, and guides from the K-Dense blog that are directly relevant to using the skills in this repository.

Start here

• Agent Skills: The Final Piece for AI-Powered Scientific Research — What Agent Skills are, why curated domain guidance beats raw model capability, and an introduction to this repository.
• K-Dense Web vs Scientific Agent Skills: Why We Built Both (And Which One You Should Use) — When the open-source skills are the right tool, and when a hosted platform with managed compute makes more sense.

Skill benchmarks and deep dives

• One Skill, 78 Databases: Why We Didn't Build 78 Skills — The design rationale behind database-lookup: consolidation cut always-on context cost by 13.9x while holding routing accuracy across five models.
• Can an AI Agent Run Your Mass Spec Pipeline? Benchmarking the PyOpenMS Skill — A 250-run study of pyopenms: 100% task success with the skill versus 96% without, 92% fewer pyOpenMS API errors, and 10% lower cost.
• Beyond RDKit: Benchmarking the Rowan Agent Skill Against Experiment — Rowan compared against RDKit and experimental data: pKa MAE 0.23 (R² 0.986), logD₇.₄ MAE 1.15, and 0.19 Å RMSD docking pose recovery for roughly $0.52 of compute.
• GPU-Accelerate Your Science: 58x Average Speedup with a Single Skill — optimize-for-gpu rewriting CPU-bound Python across 12 libraries, with speedups ranging from 1.7x to 492x.
• Towards Smarter Scientific Search: Exa Joins the Scientific Agent Skills Library — What exa-search adds: neural semantic search and URL extraction tuned for scholarly discovery instead of keyword matching.
• Benchmarking Nano Banana 2 Lite for Scientific Image Generation — A 240-image comparison of scientific-diagram models, useful when choosing a backend for generate-image: 3.8 s median latency for Nano Banana 2 Lite against 49 s for GPT Image 2, with a quality tradeoff.
• Benchmarking NVIDIA BioNeMo Agent Toolkit Skills for NIM microservices — A separate NVIDIA skill set rather than one of these, but the findings generalize: skills help most with routing to non-obvious endpoints and with weak-model reliability, and do not improve the underlying scientific model's accuracy.

Security and safe deployment

• Security in the Science Agent Era: What Every Lab Needs to Know Before Installing Skills — The practical review checklist behind this repo's Security Disclaimer: read the full SKILL.md and scripts/, scan before installing, and pin versions instead of tracking a branch.
• The Sandboxed AI Scientist: Pairing NVIDIA OpenShell with Scientific Agent Skills — Running these skills inside a policy-governed sandbox; see also the NemoClaw note in Prerequisites.

Complementary open-source projects

• Introducing Science Superpowers: Scientific Discipline for Your Research Agent — Hypothesis pre-registration, reproducible workflows, and verification-before-claims that wrap around these skills to guard against p-hacking and HARKing.
• Your AI Assistant Reasons Like a Generalist. Science Needs a Specialist. — 503 open-source AGENTS.md profiles supplying the "how to think" layer alongside the "what to do" procedures in these skills.
• Introducing mimeo and 80+ Mimeographs — Generate your own SKILL.md / AGENTS.md expert profiles by distilling how a given practitioner reasons.

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🔧 Troubleshooting

Common Issues

Problem: Skills not loading

• Verify skill folders are in the correct directory
• Each skill folder must contain a SKILL.md file
• Restart your agent/IDE after copying skills
• In Cursor, check Settings → Rules to confirm skills are discovered

Problem: Missing Python dependencies

• Solution: Check the specific SKILL.md file for required packages
• Install dependencies: uv pip install package-name

Problem: API rate limits

• Solution: Many databases have rate limits. Review the specific database documentation
• Consider implementing caching or batch requests

Problem: Authentication errors

• Solution: Some services require API keys. Check the SKILL.md for authentication setup
• Verify your credentials and permissions

Problem: Outdated examples

• Solution: Report the issue via GitHub Issues
• Check the official package documentation for updated syntax

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❓ FAQ

General Questions

Q: Is this free to use? A: Yes! This repository is MIT licensed. However, each individual skill has its own license specified in the license metadata field within its SKILL.md file—be sure to review and comply with those terms.

Q: Why are all skills grouped together instead of separate packages? A: We believe good science in the age of AI is inherently interdisciplinary. Bundling all skills together makes it trivial for you (and your agent) to bridge across fields—e.g., combining genomics, cheminformatics, clinical data, and machine learning in one workflow—without worrying about which individual skills to install or wire together.

Q: Can I use this for commercial projects? A: The repository itself is MIT licensed, which allows commercial use. However, individual skills may have different licenses—check the license field in each skill's SKILL.md file to ensure compliance with your intended use.

Q: Do all skills have the same license? A: No. Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.

Q: How often is this updated? A: We regularly update skills to reflect the latest versions of packages and APIs. Major updates are announced in release notes.

Q: Can I use this with other AI models? A: The core SKILL.md format follows the open Agent Skills standard. Installation paths, discovery, and optional metadata support vary by host and version, so confirm your target host's current documentation.

Installation & Setup

Q: Do I need all the Python packages installed? A: No! Only install the packages you need. Each skill specifies its requirements in its SKILL.md file.

Q: What if a skill doesn't work? A: First check the Troubleshooting section. If the issue persists, file an issue on GitHub with detailed reproduction steps.

Q: Do the skills work offline? A: Database skills require internet access to query APIs. Package skills work offline once Python dependencies are installed.

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💬 Support

• 📖 Documentation: Check the relevant SKILL.md and references/ folders
• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Submit a feature request
• 💼 Enterprise Support: Contact K-Dense for commercial support

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📖 Citation

If you use Scientific Agent Skills in your research or project, please cite the overall collection and, when relevant, the individual skill or skills that materially supported your work.

Recommended practice:

• Always cite Scientific Agent Skills using one of the formats below.
• Also cite each individual skill that directly contributed to your analysis, code, figures, reports, or research workflow.
• If a skill wraps or documents an external package, database, or platform, cite that upstream project too when your field's norms require it.

Collection Citation

BibTeX

@software{scientific_agent_skills_2026,
  author = {{K-Dense Inc.}},
  title = {Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents},
  year = {2026},
  url = {https://github.com/K-Dense-AI/scientific-agent-skills},
  note = {158 skills covering databases, packages, integrations, and analysis tools}
}

APA

K-Dense Inc. (2026). Scientific Agent Skills: A comprehensive collection of scientific tools for AI agents [Computer software]. https://github.com/K-Dense-AI/scientific-agent-skills

MLA

K-Dense Inc. Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents. 2026, github.com/K-Dense-AI/scientific-agent-skills.

Plain Text

Scientific Agent Skills by K-Dense Inc. (2026)
Available at: https://github.com/K-Dense-AI/scientific-agent-skills

Individual Skill Citation

When citing a specific skill, include the skill name, version from metadata.version in that skill's SKILL.md, and the direct skill URL. For example:

@software{scientific_agent_skills_astropy_2026,
  author = {{K-Dense Inc.}},
  title = {Astropy Skill for Scientific Agent Skills},
  year = {2026},
  url = {https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy},
  note = {Version 1.0, part of Scientific Agent Skills}
}

Plain text format:

Astropy skill for Scientific Agent Skills, version 1.0.
K-Dense Inc. (2026).
https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy

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📄 License

This project is licensed under the MIT License.

Copyright © 2026 K-Dense Inc. (k-dense.ai)

Key Points:

• ✅ Free for any use (commercial and noncommercial)
• ✅ Open source - modify, distribute, and use freely
• ✅ Permissive - minimal restrictions on reuse
• ⚠️ No warranty - provided "as is" without warranty of any kind

See LICENSE.md for full terms.

Individual Skill Licenses

⚠️ Important: Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License and may include additional terms or restrictions. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.