Scientific Agent Skills

Scientific Agent Skills

🔔 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.

New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handling, 100+ scientific databases, and access to all 177 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via Modal for heavy workloads. Get started here.

🎥 Webinar recording — Getting Started with K-Dense BYOK A hands-on walkthrough of K-Dense BYOK, our free, open-source AI co-scientist that runs locally on your own machine and is powered by Scientific Agent Skills. We cover how to set it up, bring your own API keys, and run real research workflows with these skills. No prior technical experience needed. Watch the recording →

Stay up to date: Follow K-Dense on X, LinkedIn, YouTube, and Reddit for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.

📄 Paper: Scientific Agent Skills is described in Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents (arXiv:2609.00065). If you use these skills in your research, please cite the paper.

A collection of 177 scientific and research skills for AI agents, created by K-Dense. The skills cover biology, chemistry, medicine, physics, engineering, Earth science, data analysis, and scientific communication. Each provides guidance for a specific package, data source, or workflow, including the scientific conventions and validation checks needed to use it.

The collection follows the open Agent Skills standard and works with Cursor, Claude Code, Codex, Google Antigravity, and other compatible hosts. It is also a portable Agent Plugins package (plugin.json + skills/), so clients that support that standard can load the collection as one plugin. Browse the illustrated skill guides, skill categories, or complete catalog to choose the skills relevant to your work.

⭐ Help make AI for science easier to discover: If Scientific Agent Skills saves you time, teaches your agent a workflow, or helps your lab move faster, please star this repository. A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community.

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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, pooled CRISPR screens, primer design, amplicon microbiomes, variant annotation, phylogenetics
• 🧪 Cheminformatics & Drug Discovery - Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization, calibrated 1D NMR processing
• 🔬 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
• 🐭 Preclinical Research & Animal Welfare - Multivariate severity scoring and humane-endpoint forecasting for laboratory animal studies, for 3Rs/refinement analysis and EU Directive 2010/63/EU reporting—an aid to severity assessment, never a decision rule
• 🖼️ Microscopy, Medical Imaging & Digital Pathology - Quantitative fluorescence microscopy, privacy-aware DICOM processing, research-only whole-slide image analysis, computational pathology, and radiology data workflows
• 🧠 Neuroscience & Electrophysiology - BIDS datasets, NWB conversion and clock alignment, extracellular recordings, and physiological signals
• 🤖 Machine Learning & AI - Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods
• 🔮 Materials Science & Chemistry - Crystal structure analysis, CALPHAD phase equilibria, metabolic modeling, computational chemistry
• 🌌 Physics & Astronomy - Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations
• ⚙️ Engineering & Simulation - Battery cycling models, chemical kinetics and ignition delay, fluid dynamics, lab hardware CAD and custom-part fabrication, discrete-event simulation, process optimization
• 📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA
• 🌊 Chemical Oceanography - Seawater carbonate chemistry, ocean acidification, mineral saturation, and measurement uncertainty
• 🌍 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, carbon-13 metabolic flux inference, biochemical kinetic models
• 🧬 Protein Engineering & Design - Protein language models, structure prediction, cryo-EM refinement, 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.

🎥 More tutorials

Recorded walkthroughs of these skills on real research tasks, from the K-Dense YouTube channel:

Video	What it covers
Skills 101: Build Your Own Scientific Agent Skill	Writing, testing, and packaging a new skill from scratch
Literature Review and Hypothesis Generation	Searching the literature and generating grounded hypotheses
Draft and Budget an Experimental Protocol	Turning a planned experiment into a costed, written protocol
Draft Responses to Reviewer Comments	Building a point-by-point rebuttal from reviewer feedback
Can AI Reproduce a Nature Medicine Paper?	An end-to-end reproduction attempt on a published analysis

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📦 What's Included

This repository provides 177 scientific and research skills organized into the following categories:

• 100+ Scientific & Financial Databases - The Database Lookup skill documents 80 databases with public, registered, or licensed access (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), including endpoint selection, pagination, and provenance. Dedicated skills cover DepMap, Imaging Data Commons, PrimeKG, NCATS ARAX, U.S. Treasury Fiscal Data, Hugging Science, OneKGPd, Genomic Intelligence, and AlphaGenome. Multi-database packages such as BioServices, Biopython, and gget 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

Every skill has a SKILL.md with its purpose, workflow, and version metadata. Depending on the workflow, it also includes code examples, reference documentation, executable helpers, or templates. Skills with bundled scripts/ have a corresponding test suite under tests/<skill-name>/ and a dependency entry in tests/skill-requirements.toml.

What's new in 2.72.0

The docx, pdf, pptx, and xlsx document skills, which were vendored from anthropics/skills under Anthropic's own license, are no longer bundled. The collection now has 177 skills; install those four from Anthropic's repository if you rely on them. Scientific Slides now builds PowerPoint decks with PptxGenJS or python-pptx and reviews them through LibreOffice and its own rendering and validation scripts.

What's new in 2.71.0

This update refreshes all 181 skills, including package and API guidance, dependency requirements, reference documentation, and validation workflows. Highlights include:

• Database retrieval: Database Lookup now catalogs 80 sources, with access requirements, retrieval contracts, and provenance checks.
• Package compatibility: Updated documented baselines include PathML 3.0.8, GeoPandas 1.2.0, and build123d 0.13.0 for lab hardware CAD. PufferLib distinguishes native 5.0, PyPI 3.0.0, and historical 4.0 workflows.
• Image generation and agent tooling: Generate Image uses the OpenRouter Image API with model discovery and request validation. Pi Agent documents Pi 0.99.2, native MCP, and updated ecosystem packages.
• RNA-seq workflow boundaries: Bulk RNA-seq prepares reads and validated counts for a PyDESeq2 handoff; Pathway Enrichment covers downstream enrichment.

Each skill records its own compatibility requirements and validation scope. Check those details before reusing an older workflow; local tests, live-service checks, and illustrative examples have different coverage.

Recently added workflows

Research task	Skills	Workflow focus
Design and analyze assays	Primer Design, FlowKit, MAGeCK, CellProfiler	PCR specificity screening, cytometry compensation and gating, pooled CRISPR contrasts, and microscopy segmentation QC
Process spectra and model metabolism	nmrglue, 13C Metabolic Flux, Tellurium	Calibrated 1D NMR, isotope-tracing inference with identifiability checks, and reproducible biochemical simulations
Reconstruct structures and preserve neural data	RELION, NWB Conversion	Cryo-EM half-map validation and two-photon imaging/behavior conversion with clock alignment
Analyze microbiomes and seawater chemistry	QIIME 2 Amplicon, Marine Carbonate Chemistry	Paired-end 16S processing with read-retention checks, carbonate speciation, and measurement uncertainty
Simulate energy and materials systems	PyBaMM, Cantera, pycalphad	Battery cycling, ignition delay, and alloy phase equilibria with numerical checks and input provenance

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📋 Table of Contents

• What's Included
• What's New in 2.72.0
• Recently Added Workflows
• Why Use This?
• Getting Started
• Security Disclaimer
• Support Open Source
• Prerequisites
• Quick Examples
• Use Cases
• Available Skills
• From the Blog
• Contributing
• Troubleshooting
• FAQ
• Support
• Citation
• License

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🚀 Why Use This?

⚡ Accelerate Your Research

• Save Days of Work - 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

• 177 Skills - Extensive coverage across all major scientific domains
• 100+ Databases - 80 databases documented by Database Lookup, plus dedicated data access skills and multi-database packages such as 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

• Simple Setup - Copy skills to your skills directory and start working
• 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
• Validation in CI - Relevant pull requests run the repository-wide structural checks and standard-library-only skill suites. Scientific dependencies are tested in separate environments; see Testing for the full validation workflow
• Community Driven - Open source with active community contributions
• Enterprise Ready - Commercial support available for advanced needs

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🎯 Getting Started

Option 1: npx (supported hosts)

Install Scientific Agent Skills with a single command:

npx skills add K-Dense-AI/scientific-agent-skills

This is a common standards-based installer for supported Agent Skills hosts, including current versions of Claude Code, Claude Cowork, Codex, Gemini CLI, Google Antigravity, and Cursor. Confirm installation paths and optional metadata behavior in your host's current documentation.

Option 2: GitHub CLI (gh skill)

If you use the GitHub CLI (v2.90.0+), you can install skills with gh skill:

# Browse and install interactively
gh skill install K-Dense-AI/scientific-agent-skills

# Install a specific skill directly
gh skill install K-Dense-AI/scientific-agent-skills scanpy

# Target a specific agent host
gh skill install K-Dense-AI/scientific-agent-skills --agent cursor
gh skill install K-Dense-AI/scientific-agent-skills --agent claude-code
gh skill install K-Dense-AI/scientific-agent-skills --agent codex
gh skill install K-Dense-AI/scientific-agent-skills --agent gemini

gh skill automatically installs to the correct directory for your agent host and records provenance metadata for supply chain integrity.

Version pinning

Pin to a specific release tag or commit SHA for reproducible installs:

# Pin to a release tag
gh skill install K-Dense-AI/scientific-agent-skills --pin v2.71.0

# Pin to a commit SHA
gh skill install K-Dense-AI/scientific-agent-skills --pin abc123def

Keeping skills up to date

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

Option 3: Agent Plugins (Cursor, Codex, and other plugin clients)

This repository is a valid Agent Plugins 1.0.0 package: root plugin.json plus Agent Skills under skills/. Clients that support the standard discover every immediate child of skills/ that contains a SKILL.md.

Cursor — symlink or copy the repo into the local plugins directory, then reload:

mkdir -p ~/.cursor/plugins/local
ln -s "$(pwd)" ~/.cursor/plugins/local/scientific-agent-skills

Restart Cursor or run Developer: Reload Window, then confirm the plugin and its skills appear under Customize. See Cursor plugins.

Codex — install from a local checkout (confirm the current CLI flag names in Codex docs):

codex plugins install .

Compatible clients (Cursor, Codex, GitHub Copilot, VS Code, Kiro, and others listed at agent-plugins.org) share the same package layout; installation UX stays client-specific.

Other Agent Skills hosts (OpenClaw, NemoClaw, Pi, Hermes, …)

Agent hosts differ in install paths, discovery settings, and support for optional frontmatter fields. npx skills add (Option 1) commonly installs into the ~/.agents/skills/ convention, with project-scoped installs under .agents/skills/; confirm both paths against your host's current documentation. To install manually on a host configured to scan one of those locations:

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git ~/.agents/skills/scientific-agent-skills   # user-level
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git .agents/skills/scientific-agent-skills      # project-level

For Hermes versions that support skill taps, add the repository as a tap:

hermes skills tap add K-Dense-AI/scientific-agent-skills

Every SKILL.md uses YAML frontmatter with a quoted metadata.version. Repository contributions must use block-style YAML; JSON-style flow mappings fail the reference validator. Optional host-specific configuration belongs under metadata, with host manifest blocks kept as nested mappings. See AGENTS.md for the complete rules. Hosts may interpret optional metadata and credential prompts differently, so verify behavior on the target host. Installing a topical subset keeps the available skill catalog focused on your work.

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.

That's it! A compatible host can discover the skills from its configured paths and use them when relevant. You can also invoke any skill manually by mentioning the skill name in your prompt.

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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. While installing the full collection was reasonable when K-Dense created and maintained every skill, the repository now includes many community contributions that we may not have reviewed as thoroughly.
• 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.
• Run the security scanner yourself. Before installing third-party skills, scan them locally:

  uv pip install cisco-ai-skill-scanner
  skill-scanner scan /path/to/skill --use-behavioral
  

• Report anything suspicious. If you find a skill that looks malicious or behaves unexpectedly, please open an issue immediately so we can investigate.

Skills are scanned weekly — incrementally, so unchanged skills carry their previous findings forward, with a full rescan of everything at least every 30 days and whenever the scanner or model changes — and the results are published to docs/security-report.md. See SECURITY.md for our security policy, what is in scope, how to report a vulnerability privately, and how to contest a scan finding. We try to address security gaps as they arise.

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❤️ Support the Open Source Community

Scientific Agent Skills is powered by 50+ incredible open source projects maintained by dedicated developers and research communities worldwide. Projects like Biopython, Scanpy, RDKit, scikit-learn, PyTorch Lightning, and many others form the foundation of these skills.

If you find value in this repository, please consider supporting the projects that make it possible:

• ⭐ Star their repositories on GitHub
• 💰 Sponsor maintainers via GitHub Sponsors or NumFOCUS
• 📝 Cite projects in your publications
• 💻 Contribute code, docs, or bug reports

👉 View the full list of projects to support

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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: Follow each skill's compatibility field and setup instructions for packages, system tools, credentials, and network access. Installing the skill files does not install their dependencies

Use a separate environment for each scientific workflow: some skills require different Python versions or incompatible package pins. uv sync installs the repository's development and validation tools, not every scientific package in the collection.

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.

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💡 Quick Examples

Once you've installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:

🧪 Drug Discovery Pipeline

Goal: Prioritize EGFR inhibitor candidates for preclinical lung-cancer research

Prompt:

Use available skills you have access to whenever possible. 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

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🔬 Single-Cell RNA-seq Analysis

Goal: Comprehensive analysis of 10X Genomics data with public data integration

Prompt:

Use available skills you have access to whenever possible. 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

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🧬 Multi-Omics Biomarker Discovery

Goal: Integrate RNA-seq, proteomics, and metabolomics to predict patient outcomes

Prompt:

Use available skills you have access to whenever possible. 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

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🎯 Virtual Screening Campaign

Goal: Discover allosteric modulators for protein-protein interactions

Prompt:

Use available skills you have access to whenever possible. 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

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🏥 Research Variant Evidence Review

Goal: Annotate a synthetic or properly de-identified VCF for hereditary-cancer research and qualified review

Prompt:

Use available skills you have access to whenever possible. 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

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🌐 Systems Biology Network Analysis

Goal: Analyze gene regulatory networks from RNA-seq data

Prompt:

…