34K+ Stars: 148 Skills to Turn Claude Code/Cursor into a Research Assistant

Researchers often spend days configuring environments, reading API documentation, and debugging errors before they can start actual analysis, whether for sequencing data, virtual drug screening, or literature review. This overhead hampers productivity across academia and industry.

What is Scientific‑Agent‑Skills?

Scientific‑Agent‑Skills, released by the K‑Dense team under an MIT license, is a modular skill library for AI agents that follow the Agent‑Skills standard. Each skill ships with a SKILL.md file containing official documentation, production‑grade code examples, best‑practice checklists, and common‑pitfall guides, allowing agents to invoke the skill without lengthy prompts.

Coverage

The library currently offers 148 skills grouped into 12 major domains, including:

Bioinformatics & Genomics (23 skills) : RNA‑seq pipelines, single‑cell analysis, differential expression, multi‑omics integration, and interfaces to tools like gget and BioPython.

Chemoinformatics & Drug Discovery (10 skills) : RDKit molecular operations, DeepChem modeling, DiffDock docking, OpenMM simulations, ADMET prediction, and lead optimization.

Clinical Research & Precision Medicine (8 skills) : ClinicalTrials.gov queries, ClinVar interpretation, COSMIC mutation lookup, DepMap dependency scores, and PyHealth data analysis.

Machine Learning & AI (14 skills) : PyTorch Lightning training, Transformers large‑model usage, scikit‑learn modeling, TimesFM zero‑shot forecasting, PyMC Bayesian modeling, and model interpretability.

Data Analysis & Visualization (21 skills) : Matplotlib, Seaborn, GeoPandas, Dask, NetworkX, and automated generation of publication‑ready figures.

Lab Automation (6 skills) : Opentrons script generation, Benchling LIMS integration, cloud‑based workflow authoring.

Additional skills cover protein engineering, quantum computing (Qiskit, PennyLane), materials analysis, remote‑sensing imagery, astrophysics simulations, engineering modeling, scientific writing, citation management, and more.

The skills can directly access over 100 authoritative scientific databases such as PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FDA, USPTO, NCBI, Reactome/KEGG, and Open‑Targets.

Case Study 1 – EGFR Inhibitor Screening (Drug Discovery)

With the skill set installed, a user can issue a single natural‑language command like:

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.

The agent automatically chains database queries, RDKit analysis, DiffDock docking, literature retrieval, and visualization, producing a complete report in minutes—a task that would normally require 2‑7 days of manual work.

Case Study 2 – Single‑Cell RNA‑seq Analysis (Bioinformatics)

Similarly, a single command can drive an end‑to‑end scRNA‑seq workflow:

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.

The agent loads the data, performs quality control, integrates public reference datasets, annotates cell types, runs differential expression, infers regulatory networks, conducts pathway enrichment, and highlights potential therapeutic targets without the user having to issue step‑by‑step prompts.

Installation Guide

Prerequisites: Python 3.13 or newer, the uv package manager, and any AI coding client that supports the Agent‑Skills specification (Cursor, Claude Code, Codex, Gemini CLI, etc.).

Three official installation methods are provided:

One‑click cross‑platform install (recommended): npx skills add K‑Dense‑AI/scientific‑agent‑skills GitHub‑CLI install for fine‑grained management: gh skill install K‑Dense‑AI/scientific‑agent‑skills Selective skill install (e.g., only Scanpy):

gh skill install K‑Dense‑AI/scientific‑agent‑skills scanpy

Agents automatically discover installed skills; users can also force a skill by name in the prompt.

Common Pitfalls & Security Risks

Do not install all 148 skills at once. Excessive skill count burdens the agent’s task‑matching engine and can cause mis‑invocations. Install only the subset relevant to your current project and review the corresponding SKILL.md for capability boundaries.

Security warning. Skills may execute code, make network requests, read/write files, and install third‑party dependencies. Always audit the skill documentation before running untrusted skills. The project team runs Cisco AI Defense Skill Scanner regularly, but researchers remain responsible for reviewing outputs and protecting sensitive data.

Sensitive data handling. Unpublished experimental data, patient privacy information, or proprietary project data must not be uploaded to external cloud agents. All generated code, analysis results, and manuscript drafts should be manually verified before use.

Prompt vs. Skill Comparison

Long prompts require the user to repeat the entire workflow, tool list, database names, and pitfalls each time; the model may forget steps, producing unstable scripts. In contrast, Scientific‑Agent‑Skills encapsulate the workflow, parameters, and best‑practice knowledge in reusable documentation, yielding reproducible, community‑validated code that can be invoked repeatedly with a single command.

Choosing the Right Skill Pack

Based on the latest GitHub star rankings (July 2026), the top skill libraries and their recommended use cases are:

Academic Research‑Skills (43 353 ⭐) – all‑round research assistant for topic planning, literature review, experiment design, writing, and revision. Ideal for graduate students.

Nature‑Skills (36 542 ⭐) – focuses on high‑impact journal manuscript structuring and submission preparation.

Scientific‑Agent‑Skills (34 134 ⭐) – emphasizes evidence tracing, database integration, and data‑analysis pipelines for STEM fields.

Other specialized packs (e.g., ARIS, AutoResearchClaw, AI‑Research‑Skills) target specific stages such as result‑to‑paper conversion, closed‑loop autonomous research, or machine‑learning‑centric workflows.

The guiding principle is to select one or two packs that match your current research stage and avoid downloading the entire collection at once.

Final Thoughts

The most expensive resource in research is not compute power but the researcher’s time. By converting decades of tacit laboratory knowledge into installable, reusable AI skills, Scientific‑Agent‑Skills turns AI agents from chat‑only assistants into disciplined, standards‑compliant research partners, freeing scientists to focus on the scientific questions themselves.

https://github.com/K‑Dense‑AI/scientific‑agent‑skills