Scientific Agent Skills: 135 AI-Powered Research Skills for Claude Code, Cursor & Codex

Published: 2026-05-17 Read: 9 min AI Research Tools / Science / Claude Code / Agent Skills

A project that recently hit GitHub Trending is turning heads in the research community — Scientific Agent Skills, developed by K-Dense (k-dense.ai). This open-source repository bundles 135 ready-to-use AI research skills spanning bioinformatics, drug discovery, clinical research, materials science, and 17+ other disciplines. Whether you use Claude Code, Cursor, Codex, or any other AI agent that supports the Agent Skills standard (agentskills.io), you can install these skills and start doing real science right away.

If you've been looking for a way to bridge scientific AI tools with your everyday coding environment — without juggling standalone scripts and Jupyter notebooks — this project deserves a close look.

What Is Scientific Agent Skills
How It Works & the Agent Skills Standard
17+ Discipline Categories at a Glance
100+ Scientific Database Integrations
Real-World Usage Scenarios
K-Dense: The Free, Open-Source AI Scientist
Getting Started
How It Compares to Other AI Research Tools
Conclusion

What Is Scientific Agent Skills

Scientific Agent Skills is a GitHub repository containing 135 pre-built Agent Skill files. Each skill is a standardized instruction set that tells an AI agent how to carry out a specific research task. These skills follow the open standard defined by agentskills.io, so any compatible AI agent can use them out of the box.

No need to write prompts from scratch, tweak parameters, or build your own workflows. A single skill file is a complete research task template — the AI agent reads it and executes the steps as defined.

        GitHub Trending Hot

        This project is currently ranking high on GitHub Trending with steadily growing stars. Community feedback has been overwhelmingly positive — especially from researchers who already work in command-line and coding environments and have been waiting for a way to seamlessly embed AI capabilities into their workflows.

How It Works & the Agent Skills Standard

Agent Skills is an open standard defined by agentskills.io, designed to make AI agent capabilities shareable and reusable like plugins. A skill is essentially a Markdown or JSON file that describes:

Task objective: What this skill is meant to do
Execution steps: The sequence of operations the AI agent should follow
Required tools: Which external APIs, databases, or CLI tools are needed
Output format: How the results should be presented

The 135 skills in Scientific Agent Skills are all written to this standard. Drop the skill files into your project's .agents/skills/ directory, and the AI agent will automatically discover and use them.

Currently supported AI agents include:

Claude Code — Anthropic's terminal-based coding assistant
Cursor — The AI-powered code editor
Codex — OpenAI's code generation tool
Any other tool compatible with the Agent Skills standard

For researchers, this means you can invoke professional-grade scientific capabilities directly from your familiar coding environment — no platform-switching required.

17+ Discipline Categories at a Glance

The most impressive aspect of Scientific Agent Skills is its disciplinary breadth. The 135 skills are distributed across 17+ major categories, covering virtually every major field of modern scientific research:

Category	What It Covers	Example Skills
Bioinformatics & Genomics	Sequence analysis, single-cell RNA-seq, gene networks	FASTA sequence alignment, differential gene expression analysis
Cheminformatics & Drug Discovery	Molecular prediction, virtual screening, ADMET property prediction	Molecular docking, drug-target interaction prediction
Proteomics & Mass Spectrometry	Protein identification, quantitative analysis	Mass spectrometry data parsing, proteome differential analysis
Clinical Research & Precision Medicine	Clinical trial design, patient stratification	Clinical trial data queries, biomarker analysis
Medical AI & Clinical ML	Disease prediction, clinical decision support	Electronic health record analysis, prognostic model building
Medical Imaging & Digital Pathology	Image segmentation, pathology slide analysis	CT/MRI image analysis, cell classification
Machine Learning & AI	Model training, hyperparameter optimization, feature engineering	Auto-modeling, cross-validation, model interpretability
Materials Science & Chemistry	Material property prediction, crystal structure analysis	Materials database queries, phase diagram computation
Physics & Astronomy	Numerical simulation, astronomical data analysis	Spectral analysis, celestial object classification
Engineering & Simulation	Finite element analysis, CFD simulation	Structural mechanics calculations, fluid dynamics simulation
Data Analysis & Visualization	Statistical analysis, chart generation	Exploratory data analysis, statistical testing
Geospatial Science & Remote Sensing	GIS analysis, satellite image processing	Land cover classification, change detection
Laboratory Automation	Experiment workflow orchestration, data acquisition	Experiment protocol generation, instrument data parsing
Scientific Communication	Literature review, scientific writing	Systematic literature search, paper structure generation
Multi-Omics & Systems Biology	Genome-proteome integrated analysis	Pathway enrichment analysis, network pharmacology
Protein Engineering & Design	Protein structure prediction, mutation design	AlphaFold result analysis, mutation stability prediction
Research Methodology	Experimental design, statistical method selection	Power analysis, sample size calculation

Each category contains multiple specific skills, totaling 135. You can install them selectively based on your research area, or deploy everything at once.

100+ Scientific Database Integrations

Analytical skills alone aren't enough — scientific research depends on data. Scientific Agent Skills comes with built-in access to 100+ scientific and financial databases, including:

PubChem — Compound structures and bioactivity data
ChEMBL — Bioactivity database for drug-like molecules
UniProt — Protein sequences and functional annotations
COSMIC — Cancer somatic mutation database
ClinicalTrials.gov — Global clinical trial registry
FRED — Federal Reserve Economic Data
USPTO — U.S. Patent database
PDB (Protein Data Bank) — 3D protein structures
NCBI Gene / GEO — Gene information and expression data
KEGG / Reactome — Biological pathways and metabolic networks

This means the AI agent doesn't just "write code for you" — it can directly query these databases, pull data, and complete the analysis locally. The entire pipeline from data retrieval to results runs seamlessly within your coding environment.

Real-World Usage Scenarios

Scenario 1: Virtual Screening in Drug Discovery

You're a computational chemist who needs to screen compounds from PubChem for potential binding to a target protein. The traditional workflow requires manually downloading compound libraries, preparing ligand files, running docking simulations, and analyzing results — easily half a day's work.

With Scientific Agent Skills, you can simply tell Claude Code:

Use the virtual-screening skill to screen candidate compounds from PubChem against the EGFR target, rank by docking score, and output the top 20 results

The AI agent automatically queries the PubChem API for compounds, prepares molecular files, runs docking calculations, and organizes the results. All you need to do is review the final output.

Scenario 2: Single-Cell RNA-seq Data Analysis

You have a 10x Genomics single-cell sequencing dataset and need to run clustering, differential gene analysis, and cell type annotation. Previously this meant writing stacks of R/Python scripts and tuning numerous parameters.

Use the single-cell-rnaseq skill to analyze the 10x data in the data/ directory — run QC, clustering, UMAP visualization, and differential gene expression analysis

The skill comes with a pre-configured standard analysis pipeline (Seurat/Scanpy), including QC thresholds, dimensionality reduction parameters, and clustering resolutions. The AI agent follows the pipeline and outputs charts and result tables.

Scenario 3: Systematic Literature Review

You're writing a review on "AI in Precision Medicine" and need to systematically search the literature, extract key information, and organize it into a structured table.

Use the systematic-literature-review skill to search for literature on AI in precision medicine from 2023–2026, extract study type, sample size, model performance, and output a PRISMA flow diagram and data extraction table

The AI agent searches PubMed, screens papers, extracts data, and even generates a PRISMA-compliant flow diagram.

Scenario 4: Clinical Trial Data Query

Use the clinical-trials-query skill to search ClinicalTrials.gov for all Phase III immunotherapy trials in non-small cell lung cancer, extracting eligibility criteria, primary endpoints, and trial status

This kind of query used to require manual searching on the website and copying results one by one. Now the AI agent calls the ClinicalTrials.gov API directly and outputs structured results.

K-Dense: The Free, Open-Source AI Scientist

Behind Scientific Agent Skills is K-Dense (k-dense.ai), a free and open-source AI co-scientist desktop application. Its core design philosophy is BYOK (Bring Your Own Key) — bring your own API key and use 40+ AI models for scientific research.

Key features of K-Dense:

Fully local execution: Your data stays on your machine at all times — nothing is uploaded to third-party servers
40+ model support: Including GPT-4o, Claude, Gemini, Llama, Mistral, and more
Built-in web search: Search literature and databases without leaving the app
File handling: Directly reads CSV, Excel, FASTA, PDB, and other scientific file formats
Optional cloud compute: Through Modal integration, elastically scale to cloud GPUs when you need heavy computation

That said, you don't need K-Dense to use these skills. As long as your AI agent supports the Agent Skills standard, just drop the skill files in and you're good to go. K-Dense simply provides a ready-to-use desktop option.

Data Security: All computation happens locally — your data never leaves your machine. This is especially important for sensitive clinical data and unpublished experimental results. If you need more compute power, you can use Modal for on-demand cloud GPUs with encrypted data transfer.

Getting Started

Getting started with Scientific Agent Skills is straightforward:

Step 1: Clone the Repository

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
cd scientific-agent-skills

Step 2: Browse Skills

All skill files are organized by discipline within the repository. Browse the directory structure to find the skills you need.

Step 3: Deploy to Your AI Agent

Copy the selected skill files into your project's .agents/skills/ directory:

# Example: Using skills in a Claude Code project
mkdir -p .agents/skills
cp -r scientific-agent-skills/bioinformatics/* .agents/skills/

Step 4: Start Using

In Claude Code, Cursor, or Codex, simply reference the skill by name, and the AI agent will follow the pre-defined workflow.

# In Claude Code
> Use the sequence-analysis skill to analyze the sequence composition in this FASTA file

How It Compares to Other AI Research Tools

There are already plenty of AI research tools out there. What sets Scientific Agent Skills apart?

Tool	What It Does	How Scientific Agent Skills Differs
ChatGPT / Claude Web	General-purpose conversational AI	Requires manual prompting, no standardized research workflows, cannot directly query databases
Jupyter Notebook + AI Plugins	Interactive computing environment	You still write all the code yourself; AI only assists with autocomplete, no complete task pipelines
Benchling / Dotmatics	Laboratory information management systems	Commercial software, expensive, limited customization, no direct AI agent integration
Elicit / Consensus	AI-powered literature search	Only covers the literature search step, doesn't extend to data analysis, experiment design, etc.
Scientific Agent Skills	Research skill library for AI agents	135 standardized skills across 17+ disciplines, 100+ database integrations, embedded in coding environments, open source and free

The core advantage of Scientific Agent Skills is standardization + integration. It's not a standalone app — it's a skill library that embeds into your existing workflow. You don't need to learn a new tool; just install a few extra skills in Claude Code or Cursor.

Conclusion

Scientific Agent Skills represents an important milestone in AI research tooling. It proves a key point: AI agent research capabilities can be standardized, modularized, and made reusable.

Three core values of this project:

Out of the box: 135 skills covering 17+ disciplines — no need to write your own prompts or build workflows from scratch
Deep integration: Plugs directly into Claude Code, Cursor, Codex, and other coding environments, with seamless access to 100+ databases
Open source & free: Built on the open Agent Skills standard, community-driven, with data security by design

If you're a researcher, computational biologist, drug discovery scientist, or anyone who does scientific work in a coding environment, this project is worth trying. Its GitHub Trending status also reflects the strong community demand for tools like this.

Repository: github.com/K-Dense-AI/scientific-agent-skills

K-Dense Website: k-dense.ai

Agent Skills Standard: agentskills.io