K-Dense-AI/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.

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.

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

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.

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.

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.

• 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

• 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

• 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)

• 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

• 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

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.

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.

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

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

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.

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.

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.

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

• 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.

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.

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

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

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

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

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

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

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.

• 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

• 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
• Variant Effect Prediction: Look up AlphaGenome Atlas AVI scores, Phred ranks, feature attributions, and per-tissue track effects for any GRCh38 SNV, or score variants and run in silico mutagenesis with the AlphaGenome model — for research prioritisation, not diagnosis
• 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 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 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

• 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

• 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
• Custom Lab Hardware Fabrication: Pre-flight STEP files, then quote CNC-machined or 3D-printed parts on Fictiv with DFM feedback and lead-time options relayed back; nothing is ordered without explicit approval of the exact total

This repository contains 177 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.

Categories overlap: a skill may appear in more than one domain, so the category counts do not sum to the 177 unique skills.

Package versions below identify the baselines documented by the skills. Follow each linked SKILL.md for its complete dependency stack, runtime requirements, and validation scope.

• RNA-seq preparation: Bulk RNA-seq (FASTQ and quantification workflows, sample/reference QC, and validated gene-level counts for a PyDESeq2 handoff)
• Sequence analysis: BioPython, pysam, scikit-bio, BioServices
• PCR assay design: Primer Design (Primer3 candidates, thermodynamics, variant masking, tails, and bounded local or BLAST-assisted specificity screening)
• Single-cell analysis: Scanpy, AnnData, scvi-tools, scVelo (RNA velocity), Arboreto, Cellxgene Census
• Genomic tools: gget, current geniml/Gtars interval workflows, deepTools, Polars-Bio, Zarr, TileDB-VCF
• Flow cytometry: FlowIO (FCS input/output), FlowKit (compensation, transforms, hierarchical gating, and FlowJo workspace analysis)
• 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)
• Variant effect prediction: AlphaGenome (DeepMind's AlphaGenome Atlas of precomputed effects for every GRCh38 SNV — AVI score with Phred rank and 18 SHAP feature attributions, plus raw and quantile scores across 9,440 RNA-seq, DNase, ATAC, ChIP, CAGE, splicing and contact-map tracks — and on-demand model scoring, in silico mutagenesis and REF-vs-ALT track prediction for human and mouse; research only, not clinical)
• Differential expression: PyDESeq2
• Pooled CRISPR screens: MAGeCK (guide counting, replicate QC, explicit contrasts, and gene hit ranking)
• Amplicon microbiomes: QIIME 2 Amplicon (paired-end 16S import, primer trimming, denoising, taxonomy, and read-retention checks)
• 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)
• Microbiome foundation models: Waypoint (Outpost Bio's open Waypoint-6m/45m/170m checkpoints, the Atlas 539k-sample MGnify pretraining corpus, and the eight-task Compass benchmark — embedding, fine-tuning, benchmarking, and pretraining on taxonomic abundance profiles, with MetaPhlAn/Kraken2/QIIME 2 conversion)

• Molecular manipulation: RDKit, Datamol, Molfeat
• NMR processing: nmrglue (calibrated 1D FIDs, phase/baseline correction, peak candidates, and signed integration)
• 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

• Spectral processing: matchms, pyOpenMS

• 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

• Severity assessment: RELSA Severity Assessment (multivariate RELSA scores from body weight, temperature, clinical/nesting scores, biomarkers, activity, heart rate, burrowing and wheel running; ARIMA humane-endpoint forecasting with 95% prediction intervals; KDE-derived attention and danger zones for 3Rs/refinement and EU Directive 2010/63/EU severity reporting) — an aid to severity assessment, never a decision rule

• 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 3.0.8
• Quantitative microscopy: CellProfiler (reusable nuclei-measurement pipeline, channel manifests, segmentation overlays, and measurement checks)
• Virtual spatial transcriptomics: noncommercial DeepSpot-M for transcriptome-wide spatial gene expression from 224x224 H&E tiles

• Data standards: BIDS (Brain Imaging Data Structure for neuroscience and biomedical datasets; pairs with DataLad for retrieval — OpenNeuro at github.com/OpenNeuroDatasets and DANDI at github.com/dandisets publish their holdings as DataLad datasets — and for BIDS-App runs under recorded provenance, with BEP028 the extension proposal for provenance records in derivatives)
• Neural recordings: Neuropixels-Analysis (extracellular spikes, silicon probes, spike sorting)
• Data conversion: NWB Conversion (two-photon TIFF and behavioral positions, clock alignment, round-trip preservation, and NWB validation)
• Physiological signals: NeuroKit2 0.2.13 for reproducible research workflows—not diagnosis, monitoring decisions, or medical-device validation

• Deep learning: PyTorch Lightning, Transformers, Stable Baselines3, and PufferLib workflows for native 5.0, published 3.0.0, and pinned historical 4.0
• 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: current split pymatgen wrapper/core plus explicitly bounded Materials Project queries
• Alloy thermodynamics: pycalphad (TDB-driven phase equilibria, phase fractions, composition checks, and database provenance)
• Metabolic modeling: COBRApy
• Astronomy: Astropy
• Quantum computing: Cirq, PennyLane, Qiskit, QuTiP 5.3.1

• Lab hardware CAD: parametric build123d 0.13.0 workflows for microfluidic chips and molds, optomechanical mounts, microplate and cuvette adapters, and behavior rigs, with interface checks and multi-view renders
• Custom-part fabrication: Fictiv (browser-driven CNC, 3D printing, sheet metal, urethane casting and molding quotes, DFM review, lead-time and region tiers, and checkout, with CAD pre-flight checks and an explicit approval gate before any order, quote request or share)
• Numerical computing: MATLAB and GNU Octave planning/review workflows, with R2026b documentation, an explicit R2026a Python-integration baseline, and GNU Octave 11.3.0
• 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
• Chemical kinetics: Cantera (homogeneous ignition delay, mechanism snapshots, numerical refinement, and conservation checks)
• Battery experiments: PyBaMM (charge/discharge protocols, parameter provenance, numerical checks, and measured-curve comparisons)
• Symbolic math: SymPy

• Marine Carbonate Chemistry: paired seawater measurements with PyCO2SYS, carbonate speciation, pH-scale handling, lab-to-in-situ corrections, aragonite/calcite saturation, and uncertainty propagation

• Visualization: Matplotlib, Seaborn, Scientific Visualization
• Geospatial analysis: GeoPandas 1.2.0 and GeoMaster (remote sensing, GIS, satellite imagery, and spatial ML)
• Data processing: Dask, Polars, Vaex
• Network analysis: NetworkX
• Document processing: LiteParse (local PDF/document parsing with bounding boxes and OCR) and MarkItDown
• 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)

• 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

• Pathway analysis: via Database Lookup (KEGG, Reactome, STRING) and PrimeKG
• Data management: LaminDB
• Biochemical dynamics: Tellurium (kinetic models, time-course simulations, perturbations, and reproducible model exchange)
• Carbon-13 metabolic flux inference: 13C Metabolic Flux (validated atom maps, steady-state isotope simulation, constrained fitting, and flux-identifiability profiles)

• Protein language models: ESM
• Experimental structure reconstruction: RELION (extracted-particle refinement, optics/STAR validation, and half-map/FSC checks)
• 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)

• 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 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 (generation, editing, and compositing through the OpenRouter Image API, with model discovery, capability checks, and dry runs)

Database Lookup documents 80 databases with public, registered, or licensed access across scientific and financial domains, with retrieval contracts, pagination/count reconciliation, and endpoint provenance. Dedicated skills cover specialized data platforms. Multi-database packages such as BioServices, Biopython, and gget add further coverage.

• Unified access: Database Lookup (80 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 (ClinGen gene-disease validity assertions and source-linked evidence for one supported GRCh38 variant, with 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)

• Cloud compute: Modal
• Data distribution and provenance: DataLad (clone and fetch OpenNeuro, DANDI and registry.datalad.org datasets over git-annex, capture re-executable provenance with datalad run/rerun and containers-run, publish to siblings)
• 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 (Pi 0.99.2 terminal harness, SDK, RPC/JSONL, native MCP, extensions, providers/models, packages, TUI components, and session tooling)

• 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

• 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.

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

• 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.
• AI Co-Scientists, Answered: 20 Questions from a Live Session with a University Research Center — Practical questions from a research center evaluating AI co-scientists: what stays open source and MIT-licensed, how local and desktop deployments work, how data is handled, and how to choose between the hosted platform and the BYOK setup that runs these skills.
• How to Use Multica for Scientific Research — A self-hosted Multica workspace plus a curated subset of these skills: clinical-trial and variant analyses, literature review, weekly autopilots, and a second-model audit, with each skill imported from skills/<name>/.

• The Silent 97%: Introducing the waypoint-bio Agent Skill — waypoint-bio against silent data loss: an unconverted MetaPhlAn table keeps 3% of abundance mass and still returns a valid embedding; skill-equipped agents won 16 to 0 on matched pairs.
• The Millimetre Problem: Introducing the lab-hardware-cad Agent Skill — lab-hardware-cad over 98 geometry-scored runs: the skill arm produced parametric, regenerable models in 49 of 49 cases (baseline 0 of 49) and named the missing Y-maze standard instead of inventing one.
• 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.

• The Model Is No Longer the Bottleneck — The case for why a repository like this one exists: frontier models now match specialized scientific software on raw capability (±0.079 ppm on NMR hydrogen shift prediction), so the limiting factor has moved to the workflow around the model — data access, code execution, verification, and auditable output.
• The AI Co-Scientist Is Here. The Bottleneck Is Verification. — A 10-point checklist for evaluating a research agent, built around exposing sources, code, data provenance, and intermediate work rather than a polished final answer — the same reasoning behind the provenance and retrieval-contract requirements in skills like database-lookup and scientific-writing.
• Reproduction, Not Generation, Is AI's Killer App for Science — Why re-running published analyses is the highest-value use of an agent: 78% of papers and 93% of individual analysis tasks reproduced across a 221-study benchmark, because a reproduction can be checked against known numbers while a generated claim cannot.
• Introducing K-Bench 01: Nine Frontier Models, 178 Real Scientific Tasks, and a Lot of Confident Wrong Answers — Nine frontier models on 178 real user tasks, with overclaiming in 40% of runs. Useful calibration for what to check when an agent reports success, and context for the verification boundaries written into the clinical, regulatory, and research-methodology skills above.

• 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 Getting Started.

• 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.
• Agentic Data Scientist: An Open Source AI That Actually Does the Analysis — A multi-agent planning, execution, and validation harness that loads these skills for end-to-end data-science workflows.
• Karpathy: An Open Source Agentic Machine Learning Engineer — An autonomous ML-training agent built to consume Scientific Agent Skills for preprocessing through hyperparameter search.

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

Read AGENTS.md before creating or changing a skill, and see CONTRIBUTING.md for the detailed workflow and pull request process. Contributions should focus on a scientific package, database, platform, or research workflow; broad coding, infrastructure, and routing skills are outside the repository's scope.

✨ 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

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

✅ 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

Every skill that ships scripts/ must have a test suite under tests/<skill-name>/ and an entry in tests/skill-requirements.toml. The tests/_meta guard checks this coverage and the shared structural contract: frontmatter, document length, local links, script syntax, bytecode, and hardcoded local paths. It also validates plugin.json against the bundled Agent Plugins schema and checks that its version matches pyproject.toml. CLI --help and worked-example execution checks belong to the individual skill suites.

# Install repository development and validation tools
uv sync

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

# Validate one skill's Agent Skills frontmatter
uv run skills-ref validate skills/<skill-name>

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

# One skill with its declared packages and Python version
uv run python tests/run_all.py --isolated <skill-name>

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

Run one skill per pytest process: different skills reuse module names such as _common, so collecting several suites together can import the wrong module. tests/run_all.py handles process isolation; --isolated also provides each skill's declared dependency environment. A suite run in the current environment may skip checks when required packages are absent.

The Skill Tests workflow runs the contract and standard-library-only suites for relevant pull requests and pushes to main, and can be triggered manually. The full scientific dependency sweep is not run in CI; run it before a release or when changing the shared contract. Some skills need external runtimes or system tools; installation gaps are documented in tests/skill-requirements.toml.

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.

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!

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

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

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.

Q: Do I need all the Python packages installed?
A: No. Install only the dependencies for the workflows you use, following each SKILL.md. Keep workflows with incompatible package or Python requirements in separate environments.

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: Local analysis can work offline once its dependencies, reference data, and model files are available. Database queries, hosted models, and cloud platforms need network access; check the individual skill's requirements.

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.

Need help? Here's how to get support:

• 📖 Documentation: Check the relevant SKILL.md and references/ folders
• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Submit a feature request
• 📣 Updates and demos: Follow X, LinkedIn, YouTube, and Reddit to keep up with new skills, tutorials, and Scientific Agent Skills releases
• 💼 Enterprise Support: Contact K-Dense for commercial support

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.

@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}
}

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

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

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

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 = {177 skills covering databases, packages, integrations, and analysis tools}
}

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 1.5, part of Scientific Agent Skills}
}

Plain text format:

Astropy skill for Scientific Agent Skills, version 1.5.
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.

This project is licensed under the MIT License.

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

• ✅ 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.

⚠️ 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.