Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 170,000+ scientists worldwide. 158 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, 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 in this repository
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.
Deterministically query 78 public scientific, biomedical, materials science, regulatory, finance, and demographics databases through documented REST APIs. Use for reproducible lookups of compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, structures, astronomy objects, environmental records, or database-backed scientific facts when endpoints, filters, pagination, and provenance need to be explicit.
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.
Skills overview
# 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) [](https://github.com/K-Dense-AI/scientific-agent-skills/actions/workflows/skill-tests.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 <a href="https://www.star-history.com/?repos=K-Dense-AI%2Fscientific-agent-skills&type=date&legend=top-left"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&theme=dark&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" /> <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" /> </picture> </a> > **🔔 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 158 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 **158 ready-to-use scientific and research skills** (covering cancer genomics, individual-level 1000 Genomes queries, hosted regulatory-sequence prediction, live pathogen-variant surveillance, analytical method validation, PK/PD modelling and dose selection, full-text biomedical and regulatory literature retrieval, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open [Agent Skills](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 & Evidence Workflows - Clinical trials, pharmacogenomics, variant evidence review, pharmacokinetic/pharmacodynamic modelling and dose-regimen evaluation, aggregate decision-support evaluation, source-bound draft report structures, and formatting of clinician-authored treatment decisions - 🧠 Healthcare AI & Biosignal Research - EHR and model research, physiological signal analysis, and retrospective validation—not patient-specific diagnosis, treatment, alarms, or deployment decisions - 🖼️ Medical Imaging & Digital Pathology - Privacy-aware DICOM processing and research-only whole-slide image analysis, computational pathology, and radiology data workflows - 🤖 Machine Learning & AI - Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods - 🔮 Materials Science & Chemistry - Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry - 🌌 Physics & Astronomy - Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations - ⚙️ Engineering & Simulation - Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization - 📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA - 🌍 Geospatial Science & Remote Sensing - Satellite imagery processing, GIS analysis, spatial statistics, terrain analysis, machine learning for Earth observation - 🧪 Laboratory Automation - Liquid handling protocols, lab equipment control, workflow automation, LIMS integration - 📚 Scientific Communication - Evidence-traceable writing, confidential authorized peer review, literature synthesis, document processing, macro-free PPTX posters, slides, schematics, and citation management - 🔬 Multi-omics & Systems Biology - Multi-modal data integration, pathway analysis, network biology, systems-level insights - 🧬 Protein Engineering & Design - Protein language models, structure prediction, sequence design, function annotation - 🧰 Agent Platforms & Infrastructure - Build on Pi with SDK, RPC, extensions, custom providers/models, packages, TUI components, and session tooling - 🎓 Research Methodology - Evidence-bounded candidate hypotheses, scientific brainstorming, critical thinking, grant writing, and qualitative low-stakes evaluation of scholarly works - ⚖️ Regulatory & Standards - Draft evidence-preparation artifacts for ISO management-system and laboratory standards, plus analytical method validation, verification, and transfer under ICH/USP/CLSI frameworks—prepared for qualified review, never a certification, accreditation, or method-release decision **Transform your AI coding agent into an 'AI Scientist' on your desktop!** > 🎬 **New to Scientific Agent Skills?** Watch our [Getting Started with Scientific Agent Skills](https://youtu.be/ZxbnDaD_FVg) video for a quick walkthrough. --- ## 📦 What's Included This repository provides **158 scientific and research skills** organized into the following categories: - **100+ Scientific & Financial Databases** - A unified database-lookup skill provides deterministic, provenance-rich access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, U.S. Treasury Fiscal Data, Hugging Science, OneKGPd, and Genomic Intelligence. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage - **70+ Optimized Python Package Skills** - Explicitly defined, version-aware workflows for RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, PathML, pydicom, NeuroKit2, PufferLib, QuTiP, GeoPandas, pymatgen, BioPython, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), and others. The agent can still use *any* Python package; these skills provide
What people ask about scientific-agent-skills
What is K-Dense-AI/scientific-agent-skills?
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K-Dense-AI/scientific-agent-skills is skills for the Claude AI ecosystem. Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 170,000+ scientists worldwide. 158 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. It has 32k GitHub stars and was last updated today.
How do I install scientific-agent-skills?
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You can install scientific-agent-skills by cloning the repository (https://github.com/K-Dense-AI/scientific-agent-skills) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is K-Dense-AI/scientific-agent-skills safe to use?
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Our security agent has analyzed K-Dense-AI/scientific-agent-skills and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains K-Dense-AI/scientific-agent-skills?
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K-Dense-AI/scientific-agent-skills is maintained by K-Dense-AI. The last recorded GitHub activity is from today, with 6 open issues.
Are there alternatives to scientific-agent-skills?
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Yes. On ClaudeWave you can browse similar skills at /categories/skills, sorted by popularity or recent activity.
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