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Scientific Agent Skills

License: MIT arXiv Version Skills Databases Agent Skills Agent Plugins Security Scan Skill Tests Works with X LinkedIn YouTube Reddit

🔔 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 166 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 comprehensive collection of 166 ready-to-use scientific and research skills (covering cancer genomics, individual-level 1000 Genomes queries, genome-wide precomputed variant effects from the AlphaGenome Atlas, 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, bounded biomedical knowledge graph search, molecular dynamics, RNA velocity, microbiome foundation models, 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. The repository is also a portable Agent Plugins package (plugin.json + skills/), so plugin-capable clients can load the whole collection as one plugin. 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.

⭐ 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.

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 - 🐭 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 - 🖼️ 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.

🎥 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

📦 What's Included

This repository provides 166 scientific and research skills organized into the following categories:

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/ — CI blocks a pull request that adds bundled tooling without one

📋 Table of Contents

🚀 Why Use This?

⚡ Accelerate Your Research

🎯 Comprehensive Coverage

🔧 Easy Integration

🌟 Maintained & Supported

🎯 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 v2.66.0

# 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 has YAML frontmatter, but legacy and community skills vary in metadata formatting (block or flow style) and optional extension fields. Repository updates must keep metadata.version as a quoted numeric string and pass canonical skills-ref validate ./skills/<skill-name> checks. Hosts may interpret optional metadata and credential prompts differently, so verify behavior on the target host. Because 166 skills add up to a lot of standing context, consider installing a topical subset rather than the whole collection.

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.

⚠️ 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:

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.

❤️ 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:

👉 View the full list of projects to support

🙏 Skill Credits

The docx, pdf, pptx, and xlsx document skills are created and maintained by Anthropic and vendored here from anthropics/skills. They are used under Anthropic's terms — see each skill's LICENSE.txt — and we track upstream so you get their latest improvements. All credit for those four skills goes to Anthropic.

⚙️ Prerequisites

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.

💡 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

🔬 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

🧬 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

🎯 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

🏥 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

🌐 Systems Biology Network Analysis

Goal: Analyze gene regulatory networks from RNA-seq data

Prompt:

Use available skills you have access to whenever possible. 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.

🔬 Use Cases

🧪 Drug Discovery & Medicinal Chemistry

🧬 Bioinformatics & Genomics

🏥 Clinical Research & Evidence Workflows

🔬 Multi-Omics & Systems Biology

📊 Data Analysis & Visualization

🧪 Laboratory Automation

📚 Available Skills

This repository contains 166 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.

Skill Categories

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 (28 skills)

🧪 Cheminformatics & Drug Discovery (10 skills)

🔬 Proteomics & Mass Spectrometry (2 skills)

🏥 Clinical Research & Evidence Workflows (8 skills)

🐭 Preclinical Research & Animal Welfare (1 skill)

🖼️ Medical Imaging & Digital Pathology (4 skills)

🧠 Neuroscience & Electrophysiology (3 skills)

🤖 Machine Learning & AI (14 core skills)

🔮 Materials Science, Chemistry & Physics (7 skills)

⚙️ Engineering & Simulation (6 skills)

📊 Data Analysis & Visualization (22 skills)

🧪 Laboratory Automation (6 skills)

🔬 Multi-omics & Systems Biology (3 skills)

🧬 Protein Engineering & Design (4 skills)

📚 Scientific Communication (27 skills)

🔬 Scientific Databases & Data Access (12 skills → 100+ databases total)

A unified database-lookup skill provides deterministic REST API access to 78 public databases across all domains, with retrieval contracts, pagination/count reconciliation, and endpoint provenance. Dedicated skills cover specialized data platforms. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage. - 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) - Public germline variant evidence: Folklore Variant Evidence (one supported GRCh38 variant to structured source-linked evidence, automated variant-level ACMG/AMP decision support, explicit ambiguity handling, and related literature for qualified professional review) - Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index) - Knowledge graph: PrimeKG (precision medicine knowledge graph — genes, drugs, diseases, phenotypes) - Biomedical knowledge graph search: NCATS ARAX (bounded, Biolink-constrained one-hop and endpoint-pinned two-hop queries over knowledge graphs with up to five explicitly selected NCATS Translator providers, with provenance preservation) - 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) - Precomputed variant effects: AlphaGenome (Atlas lookups for every GRCh38 SNV with AVI, Phred, SHAP feature attributions, and per-track scores; on-demand AlphaGenome model scoring for indels, mouse, and custom scorers; Atlas website deep links) - 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 (12 skills)

🎓 Research Methodology & Planning (13 skills)

⚖️ Regulatory & Standards (2 skills)

📖 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.

📝 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

Skill benchmarks and deep dives

Why the workflow layer matters

Security and safe deployment

Complementary open-source projects

🤝 Contributing

We welcome contributions to expand and improve this scientific skills repository!

For detailed instructions on adding or updating a skill, see CONTRIBUTING.md. The guide covers repository structure, required SKILL.md frontmatter, Agent Skills specification requirements, versioning, validation, security scanning, and pull request expectations.

Ways to Contribute

✨ Add New Skills - Create skills for additional scientific packages or databases - Add integrations for scientific platforms and tools

📚 Improve Existing Skills - Enhance documentation with more examples and use cases - Add new workflows and reference materials - Improve code examples and scripts - Fix bugs or update outdated information

🐛 Report Issues - Submit bug reports with detailed reproduction steps - Suggest improvements or new features

How to Contribute

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-skill)
  3. Follow CONTRIBUTING.md and the existing directory structure
  4. Ensure all new skills include valid SKILL.md files with required frontmatter and metadata.version
  5. Test your examples and workflows thoroughly, and add a suite under tests/<skill-name>/ if your skill ships scripts/
  6. Commit your changes (git commit -m 'Add amazing skill')
  7. Push to your branch (git push origin feature/amazing-skill)
  8. Submit a pull request with a clear description of your changes

Contribution Guidelines

✅ Adhere to the Agent Skills Specification — Every skill must follow the official spec (valid SKILL.md frontmatter, naming conventions, directory structure)
✅ Include a quoted metadata.version value in every SKILL.md
✅ Increment metadata.version when updating an existing skill
✅ Maintain consistency with existing skill documentation format
✅ Ensure all code examples are tested and functional
✅ Follow scientific best practices in examples and workflows
✅ Update relevant documentation when adding new capabilities
✅ Provide clear comments and docstrings in code
✅ Include references to official documentation

Testing

Every skill that ships scripts/ must have a test suite under tests/<skill-name>/ and an entry in tests/skill-requirements.toml. This is enforced — tests/_meta fails a pull request that adds bundled tooling without one, and it also runs a repo-wide structural contract over all skills (frontmatter conformance, SKILL.md length, local links resolving, scripts parsing, no shipped bytecode, no hardcoded local paths, --help behavior).

# Structural contract and coverage guard — seconds, no scientific packages needed
uv run python -m pytest tests/_meta -q

# One skill's suite
uv run --with pytest python -m pytest tests/<skill-name> -q

# Every suite, each in its own throwaway environment
uv run python tests/run_all.py --isolated

The Skill Tests workflow runs the contract plus the standard-library-only suites on every pull request; the full --isolated sweep builds ~100 environments and is run locally or on a schedule.

Security Scanning

All skills in this repository are security-scanned using Cisco AI Defense Skill Scanner, an open-source tool that detects prompt injection, data exfiltration, and malicious code patterns in Agent Skills.

If you are contributing a new skill, we recommend running the scanner locally before submitting a pull request:

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

Note: A clean scan result reduces noise in review, but does not guarantee a skill is free of all risk. Contributed skills are also reviewed manually before merging.

Recognition

Contributors are recognized in our community and may be featured in: - Repository contributors list - Special mentions in release notes - K-Dense community highlights

Your contributions help make scientific computing more accessible and enable researchers to leverage AI tools more effectively!

Support Open Source

This project builds on 50+ amazing open source projects. If you find value in these skills, please consider supporting the projects we depend on.

🔧 Troubleshooting

Common Issues

Problem: Skills not loading - Verify skill folders are in the correct directory (see Getting Started) - 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

Problem: gh skill install or docs link to scientific-skills/ fails (v2.43.0+) - As of v2.43.0, skills live under skills/ (not scientific-skills/) to match the Agent Skills layout expected by GitHub CLI - Update manual copy paths, bookmarks, and citations from scientific-skills/<name> to skills/<name> - Re-run gh skill install K-Dense-AI/scientific-agent-skills after pulling the latest release

❓ 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.

Contributing

Q: Can I contribute my own skills?
A: Absolutely! We welcome contributions. See the Contributing section for guidelines and best practices.

Q: How do I report bugs or suggest features?
A: Open an issue on GitHub with a clear description. For bugs, include reproduction steps and expected vs actual behavior.

💬 Support

Need help? Here's how to get support:

📖 Citation

If you use Scientific Agent Skills in your research or project, please cite our paper:

Timothy Kassis, Vinayak Agarwal, Yuhuan He, Darshil Patel, and Aubrey M. Brueckner. Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065, 2026. https://arxiv.org/abs/2609.00065

When relevant, also cite the individual skill or skills that materially supported your work.

GitHub's Cite this repository button, backed by CITATION.cff, produces the same paper citation in APA or BibTeX.

The paper citation helps others find the repository, understand the broader skill ecosystem used in your workflow, and credit the maintenance effort behind Scientific Agent Skills. Individual skill citations give more precise credit for the specific package, database, or workflow guidance your agent used.

Recommended practice: - Always cite the Scientific Agent Skills paper 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.

Paper Citation

BibTeX

@misc{kassis2026scientificagentskills,
  title         = {Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents},
  author        = {Kassis, Timothy and Agarwal, Vinayak and He, Yuhuan and Patel, Darshil and Brueckner, Aubrey M.},
  year          = {2026},
  eprint        = {2609.00065},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.00065}
}

APA

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A library of procedural knowledge for research agents. arXiv. https://arxiv.org/abs/2609.00065

MLA

Kassis, Timothy, et al. "Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents." arXiv, 2026, arxiv.org/abs/2609.00065.

Plain Text

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://arxiv.org/abs/2609.00065

Software Citation

If you also need to cite a specific version of the repository itself (for example, to pin the exact skill set an analysis ran against), add a software citation alongside the paper and record the release tag or commit you used:

@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 = {166 skills covering databases, packages, integrations, and analysis tools}
}

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

We appreciate acknowledgment in publications, presentations, or projects that benefit from these skills.

📄 License

This project is licensed under the MIT License.

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

Key Points:

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.