Project Overview
Scientific Agent Skills is an open-source collection of 133 installable AI agent skills โ covering scientific databases, Python package wrappers, and analysis workflows โ that transforms general-purpose AI coding assistants into domain-expert research tools for biology, chemistry, and clinical science.
Project Background & Positioning
Why It Exists
Modern AI coding agents like Claude Code and Cursor are powerful generalists, but they lack out-of-the-box knowledge of the hundreds of specialized APIs, databases, and scientific libraries researchers depend on daily. Setting up individual integrations for PubChem, UniProt, ChEMBL, RDKit, Scanpy, and similar tools requires significant boilerplate โ time researchers would rather spend on science. Scientific Agent Skills packages that institutional knowledge as first-class, reusable skills that any compatible agent can install in a single command.
How It Differs From Similar Projects
Most scientific toolkits target Python developers directly; they ship libraries, not agent behaviors. Scientific Agent Skills ships instructions โ structured skill definitions that tell an AI agent how to use a tool correctly, not just that the tool exists. Unlike ad-hoc prompt engineering, each skill is versioned, security-scanned via Cisco AI Defense Skill Scanner, and published under a consistent open standard (agentskills.io), making the collection auditable and enterprise-ready. The platform-agnostic Agent Skills standard means the same skill works across Claude Code, Cursor, Codex, and any future compliant agent โ no vendor lock-in.
Functional Categories
๐งฌ Scientific Database Skills โ 100+ skills
Representative examples: PubChem (compound lookup), ChEMBL (bioactivity data), UniProt (protein sequences), COSMIC (cancer mutations), ClinicalTrials.gov (clinical study search).
One-line purpose: Give agents direct, structured access to the world's leading scientific data repositories.
๐ Python Package Skills โ 70+ skills
Representative examples: RDKit (cheminformatics), Scanpy (single-cell RNA-seq), PyTorch Lightning (deep learning workflows), scikit-learn (classical ML), BioPython (sequence analysis).
One-line purpose: Pre-wire optimized usage patterns for the most important scientific Python libraries so agents apply them correctly on first use.
๐ Integration Skills โ 9 skills
Representative examples: Benchling (lab data management), DNAnexus (genomics cloud platform), electronic lab notebook connectors.
One-line purpose: Bridge AI agents to enterprise scientific platforms used in production research environments.
๐ Analysis & Communication Skills โ 30+ skills
Representative examples: Literature review automation, scientific writing assistance, data visualization guidance, multi-omics pipeline orchestration, experiment reporting.
One-line purpose: Handle the end-to-end research workflow from data ingestion through publication-ready outputs.
Core Highlights
Platform-agnostic Agent Skills standard โ Skills are defined using the open agentskills.io specification, so the same library works with Claude Code, Cursor, Codex, and any future agent that adopts the standard. Researchers are not locked into a single vendor.
Security-first distribution โ Every skill in the collection is scanned with Cisco AI Defense Skill Scanner on an approximately weekly cadence. The documentation prominently warns users to review skills before installation, and selective install is encouraged over bulk adoption.
Breadth across 13+ scientific domains โ From drug discovery and genomics to materials science, engineering simulation, and clinical research, the 133 skills cover the full breadth of computational science. Teams in diverse research verticals can find relevant tools without building their own.
Single-command installation โ The entire collection (or any subset) installs via npx skills add or the GitHub CLI gh skill install. No Python environment setup or configuration boilerplate is required before use.
MIT license with community contribution model โ Any researcher or developer can add skills following the Agent Skills specification. The project's 429+ commits across 74 releases reflects active, ongoing maintenance by K-Dense Inc. and the broader community.
Hybrid offline/online operation โ Skills are designed to work both locally (offline, using installed Python packages) and with live API calls depending on what is available, making them usable in air-gapped or resource-constrained environments.
Use Cases by Role
General Developers
Developers building scientific applications or internal research tooling can use the skills as reference implementations for integrating databases like PubChem or UniProt, drastically shortening integration time. The MIT license permits embedding skills in commercial products.
Data & Research
Bench scientists and computational researchers are the primary audience. A bioinformatician can install the Scanpy and single-cell database skills and immediately ask their agent to run a complete RNA-seq analysis pipeline referencing published datasets. A medicinal chemist can query ChEMBL bioactivity data and run RDKit structure-activity analysis within the same agent session, without switching tools.
DevOps / Platform Engineers
Teams managing AI agent infrastructure for research organizations can use the enterprise support from K-Dense Inc. and the Cisco AI Defense integration to deploy the skill library with confidence across a regulated environment. Security scanning artifacts and version history provide audit trails required in GxP or clinical settings.
Product Teams
Product managers building AI-assisted research products can use the skill collection to rapidly prototype domain-specific agent features โ a drug discovery copilot, a clinical evidence summarizer, or a multi-omics analysis assistant โ without staffing specialized prompt engineers for each scientific subdomain.
Quick Start
๐ How to find resources
Browse the full skill catalog and documentation on the GitHub repository:
https://github.com/K-Dense-AI/scientific-agent-skills
Skills are organized by domain category (databases, Python packages, integrations, analysis). The README provides a searchable index of all 133 skills with brief descriptions.
๐ How to install / integrate
Install all skills with a single command using npx:
npx skills add K-Dense-AI/scientific-agent-skills
Or install via the GitHub CLI:
gh skill install K-Dense-AI/scientific-agent-skills
To install a specific skill subset rather than the full collection, follow the selective install instructions in the repository documentation. After installation, your agent (Claude Code, Cursor, etc.) will immediately recognize and apply the installed skills in relevant scientific contexts.
๐ค How to contribute
Fork the repository, create a new skill following the Agent Skills specification, then open a pull request:
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
cd scientific-agent-skills
# Create your skill following the spec at agentskills.io
git checkout -b skill/your-tool-name
git add .
git commit -m "feat: add skill for YourToolName"
git push origin skill/your-tool-name
# Open a PR on GitHub
Contributions must pass the Cisco AI Defense Skill Scanner before merge.
Project Structure
scientific-agent-skills/
โโโ databases/ # 100+ scientific database skill definitions
โ โโโ pubchem/ # PubChem compound and bioassay skills
โ โโโ chembl/ # ChEMBL bioactivity skills
โ โโโ uniprot/ # UniProt protein data skills
โ โโโ ... # 97+ additional database skills
โโโ packages/ # 70+ Python package wrapper skills
โ โโโ rdkit/ # Cheminformatics workflows
โ โโโ scanpy/ # Single-cell RNA-seq analysis
โ โโโ pytorch-lightning/ # Deep learning pipeline skills
โ โโโ ... # 67+ additional package skills
โโโ integrations/ # 9 enterprise platform integration skills
โ โโโ benchling/ # Lab data management
โ โโโ dnanexus/ # Genomics cloud platform
โโโ analysis/ # 30+ analysis & communication skills
โ โโโ literature/ # Literature review automation
โ โโโ visualization/ # Scientific data visualization
โโโ docs/ # Documentation, examples, contribution guide
โโโ README.md # Full skill catalog and quick-start guide
Related Ecosystem
Upstream platforms and tools
- agentskills.io โ The open Agent Skills standard that defines the skill specification this library implements
- Claude Code (Anthropic) โ Primary AI agent for which many skills were developed and tested
- Cursor / Codex โ Additional compatible AI coding assistants
- Cisco AI Defense Skill Scanner โ Security scanning infrastructure used to verify all published skills
Complementary projects
- RDKit โ Open-source cheminformatics; wrapped by multiple skills in this collection
- Scanpy / AnnData โ Single-cell genomics ecosystem; wrapped by dedicated skills
- BioPython โ General bioinformatics library; wrapped by sequence analysis skills
- Benchling / DNAnexus โ Enterprise scientific platforms with dedicated integration skills
License
MIT License
| Use Case | |
|---|---|
| โ | Use skills in personal, academic, and commercial projects |
| โ | Modify and redistribute skills with attribution |
| โ | Embed skills in proprietary products |
| โ | Fork and create derivative skill collections |
| โ | Remove attribution notices from redistributed copies |
| โ | Hold K-Dense Inc. liable for outcomes in production research or clinical environments |
| โน๏ธ | Individual skills may call third-party APIs (PubChem, ChEMBL, etc.) subject to those services' own terms of use |
| โน๏ธ | Skills that execute code in your environment can affect agent behavior โ review before production deployment |
FAQ
Q: Do I need a K-Dense account to use the skills?
A: No. The full skill collection is freely available under the MIT license via GitHub. A K-Dense account is only needed for enterprise support, which is an optional paid tier.
Q: Will the skills work with AI agents other than Claude Code?
A: Yes. Skills are built on the open agentskills.io standard, so they are compatible with any AI agent that implements the specification, including Cursor and Codex. New compatible agents will automatically gain access as vendors adopt the standard.
Q: How are skills kept secure?
A: K-Dense runs all skills through the Cisco AI Defense Skill Scanner approximately weekly. The documentation advises users to install only the skills they need, read each skill's documentation before use, and optionally run the scanner locally for additional assurance.
Q: Can I install only a subset of skills instead of all 133?
A: Yes. Selective installation is documented in the repository and is the recommended approach for production environments. This limits the agent's capabilities to only what is necessary, reducing both attack surface and agent confusion.
Q: Are the skills usable without an internet connection?
A: Partially. Skills that wrap installed Python packages (RDKit, scikit-learn, etc.) operate fully offline. Skills that query live scientific APIs (PubChem, UniProt, ClinicalTrials.gov) require network access. The documentation for each skill clearly states whether it depends on external services.
Quick Links
- GitHub Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Agent Skills Standard: https://agentskills.io
- K-Dense Inc.: https://kdense.ai
- Community Slack: listed in the repository README
Summary
Scientific Agent Skills closes the gap between powerful AI coding agents and the specialized scientific infrastructure researchers depend on, delivering 133 production-ready, security-scanned skills across biology, chemistry, and clinical science in a single installable package. Its adherence to the open Agent Skills standard ensures longevity and cross-platform compatibility, while the MIT license makes it suitable for everything from academic prototyping to enterprise research pipelines. Computational biologists, medicinal chemists, data scientists, and research platform teams building AI-assisted workflows will find this the fastest path from a blank agent to a capable scientific collaborator.