Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Antigravity, and the open Agent Skills standard.
Scientific Agent Skills is a Python library of 144 pre-built skill definitions that give AI coding agents structured, documented access to scientific workflows spanning cancer genomics, molecular docking, single-cell RNA sequencing, ADMET drug analysis, DICOM medical imaging, geospatial remote sensing, and laboratory automation, among other domains. It connects to Claude Code natively (and also supports Cursor, Codex, and Google Antigravity) by following the open Agent Skills standard, which supplies each skill as curated documentation and examples that steer the agent toward correct, reproducible tool usage rather than improvised code. Over 100 scientific databases are covered, including sources relevant to drug discovery, proteomics, and clinical genomics. A notable companion project, K-Dense BYOK, packages these skills into a free desktop research workspace where users supply their own API keys, choose from 40-plus models, and can optionally offload heavy computation to Modal. The library is aimed at computational biologists, cheminformaticians, clinical researchers, and data scientists who run multi-step scientific workflows inside an AI coding agent.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/scientific-agent-skills24 items en este repositorio
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools.
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me think through this from all sides", "council mode", "mind council", or "deliberate on this". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD), earth/environment (USGS, NOAA, EPA), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, ZINC, BindingDB), materials (Materials Project, COD), biology/genomics (Reactome, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, BioGRID, Gene Ontology, dbSNP, gnomAD, ENCODE, Human Protein Atlas, Human Cell Atlas), disease/clinical (COSMIC, Open Targets, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, World Bank, US Treasury), demographics (US Census, Eurostat, WHO). Use when looking up compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, or any public database API query.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
Resumen de Skills
# Scientific Agent Skills [](LICENSE.md) [](pyproject.toml) [](#-whats-included) [](#-whats-included) [](https://agentskills.io/) [](https://github.com/K-Dense-AI/scientific-agent-skills/actions/workflows/security-scan.yml) [](#-getting-started) [](https://x.com/k_dense_ai) [](https://www.linkedin.com/company/k-dense-inc) [](https://www.youtube.com/@K-Dense-Inc) ## Star History [](https://www.star-history.com/#K-Dense-AI/scientific-agent-skills&type=date&legend=top-left) > **🔔 Claude Scientific Skills is now Scientific Agent Skills.** Same skills, broader compatibility — now works with any AI agent that supports the open [Agent Skills](https://agentskills.io/) standard, not just Claude. > **New: [K-Dense BYOK](https://github.com/K-Dense-AI/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 147 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via [Modal](https://modal.com/) for heavy workloads. [Get started here.](https://github.com/K-Dense-AI/k-dense-byok) > **Stay up to date:** Follow K-Dense on [X](https://x.com/k_dense_ai), [LinkedIn](https://www.linkedin.com/company/k-dense-inc), and [YouTube](https://www.youtube.com/@K-Dense-Inc) for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent. A comprehensive collection of **147 ready-to-use scientific and research skills** (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open [Agent Skills](https://agentskills.io/) standard, created by [K-Dense](https://k-dense.ai). 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](https://github.com/K-Dense-AI/scientific-agent-skills). 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 & Precision Medicine - Clinical trials, pharmacogenomics, variant interpretation, drug safety, clinical decision support, treatment planning - 🧠 Healthcare AI & Clinical ML - EHR analysis, physiological signal processing, medical imaging, clinical prediction models - 🖼️ Medical Imaging & Digital Pathology - DICOM processing, whole slide image analysis, computational pathology, radiology 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 - Literature review, peer review, scientific writing, document processing, posters, slides, schematics, 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 - Hypothesis generation, scientific brainstorming, critical thinking, grant writing, scholar evaluation **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](https://youtu.be/ZxbnDaD_FVg) video for a quick walkthrough. --- ## 📦 What's Included This repository provides **147 scientific and research skills** organized into the following categories: - **100+ Scientific & Financial Databases** - A unified database-lookup skill provides direct access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, U.S. Treasury Fiscal Data, and Hugging Science (curated catalog of scientific datasets, models, and demos across 17 scientific domains on Hugging Face). Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage - **70+ Optimized Python Package Skills** - Explicitly defined skills for RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, pyzotero, BioServices, PennyLane, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, TimesFM, and others — with curated documentation, examples, and best practices. Note: the agent can write code using *any* Python package, not just these; these skills simply provide stronger, more reliable performance 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, scientific writing, peer review, document processing, Paperzilla, PACSOMATIC, Exa Search, posters, slides, schematics, infographics, Mermaid diagrams, and more - **10+ Research & Clinical Tools** - Hypothesis generation, grant writing, clinical decision support, treatment plans, BIDS, regulatory compliance, scenario analysis, and workflow-derived skill drafting with Autoskill Each skill includes: - ✅ Comprehensive documentation (`SKILL.md`) - ✅ Practical code examples - ✅ Use cases and best practices - ✅ Integration guides - ✅ Reference materials --- ## 📋 Table of Contents - [What's Included](#-whats-included) - [Why Use This?](#-why-use-this) - [Getting Started](#-getting-started) - [Security Disclaimer](#%EF%B8%8F-security-disclaimer) - [Support Open Source](#%EF%B8%8F-support-the-open-source-community) - [Prerequisites](#%EF%B8%8F-prerequisites) - [Quick Examples](#-quick-examples) - [Use Cases](#-use-cases) - [Available Skills](#-available-skills) - [Contributing](#-contributing) - [Troubleshooting](#-troubleshooting) - [FAQ](#-faq) - [Support](#-support) - [Citation](#-citation) - [License](#-license) --- ## 🚀 Why Use This? ### ⚡ **Accelerate Your Research** - **Save Days of Work** - Skip API documentation research and integration setup - **Production-Ready Code** - Tested, validated examples following scientific best practices - **Multi-Step Workflows** - Execute complex pipelines with a single prompt ### 🎯 **Comprehensive Coverage** - **147 Skills** - Extensive coverage across all major scientific domains - **100+ Database
Lo que la gente pregunta sobre scientific-agent-skills
¿Qué es K-Dense-AI/scientific-agent-skills?
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K-Dense-AI/scientific-agent-skills es skills para el ecosistema de Claude AI. Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Antigravity, and the open Agent Skills standard. Tiene 28.1k estrellas en GitHub y se actualizó por última vez today.
¿Cómo se instala scientific-agent-skills?
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Puedes instalar scientific-agent-skills clonando el repositorio (https://github.com/K-Dense-AI/scientific-agent-skills) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar K-Dense-AI/scientific-agent-skills?
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Nuestro agente de seguridad ha analizado K-Dense-AI/scientific-agent-skills y le ha asignado un Trust Score de 100/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene K-Dense-AI/scientific-agent-skills?
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K-Dense-AI/scientific-agent-skills es mantenido por K-Dense-AI. La última actividad registrada en GitHub es de today, con 29 issues abiertos.
¿Hay alternativas a scientific-agent-skills?
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Sí. En ClaudeWave puedes explorar skills similares en /categories/skills, ordenados por popularidad o actividad reciente.
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