Scientific Agent Skills is a library of 177 ready-made agent skills that turn a general-purpose AI coding agent into a research assistant for biology, chemistry, medicine and drug discovery. It is aimed at scientists, bioinformaticians and data people who already work inside Cursor, Claude Code, Codex or Google Antigravity and would rather have the agent follow a documented procedure than invent one. The repository is MIT licensed, mostly Python, and has gathered roughly 53,500 stars and 4,700 forks since it was created in October 2025. It was previously called Claude Scientific Skills: the rename marks its move to the open Agent Skills standard, so it is no longer tied to one vendor's agent.
What it does
The project is content more than application. You install it into an agent that supports skills, and the agent gains a catalog of procedures it can pull in when a task matches one of them.
- 177 skills, a figure from the project's own badge and README, spanning genomics, proteomics, metabolomics, cheminformatics, clinical research, computational biology, materials science and scientific visualization (the repository's GitHub topics name those areas).
- Access to more than 100 scientific databases, so a question about a gene, a compound or a trial is answered from a lookup instead of from the model's memory.
- Curated documentation and worked examples inside each skill, which is what keeps the agent on a known workflow rather than a plausible-looking improvisation.
The project also says it is used by more than 250,000 scientists worldwide. That is the authors' own claim, not an independent count; the publicly measurable numbers here are the stars and forks.
How it works
Agent Skills is an open format: a skill is a folder of instructions and supporting files that the host application loads when a request looks relevant, so the model reads the project's documentation before it starts writing code or queries. This repository publishes its skills in that format and also advertises compatibility with the Agent Plugins standard, which is how one source tree can serve Cursor, Claude Code, Codex, Pi and Antigravity.
The repository treats the skills as software rather than as a pile of prompts. The README carries badges for two GitHub Actions workflows, a security scan and a skill-test suite, plus a version number (2.72.0 in the saved copy) and a link to an arXiv preprint. "Validated" in the project's description refers to its own checks: nobody outside the project has certified the 177 skills.
Getting started
The code lives at github.com/K-Dense-AI/scientific-agent-skills and the license is MIT, so the library itself costs nothing. What costs money is the intelligence behind it: you need a host that already pays for a model, whether that is a Cursor or Claude Code subscription, an API key billed per token, or a local model if your host supports one. These skills do not change that arithmetic, and a long bioinformatics session against a large model is not cheap.
The real prerequisite is a host application that implements Agent Skills, listed as Cursor, Claude Code, Codex, Pi, Antigravity or anything else supporting the standard. The main language is Python, so expect to have a working Python environment for the skills that run analysis code. Beyond that the material lists no hardware requirements: no operating system, memory or GPU minimum is given, and nothing in the saved README says which of the 100-plus databases need a free registration or their own API key. The copy of the README available here is cut off before the installation steps, so take the exact command from the repository's Getting Started section rather than from this page. The README also teases a hosted "K-Dense BYOK" option, but the saved text stops before explaining what it includes.
When to use it / when not
The catch is the one the channel's own write-up names: without a host that supports Agent Skills there is nothing here you can plug in, and a skill library is only as useful as the host's willingness to load the right file at the right moment.
Use it instead of just asking Claude directly when the task has a correct procedure you do not want reinvented: a differential expression run, a structure or compound lookup, a figure that has to follow a field's conventions. Don't use it if you mostly need scientific prose or literature summaries, or if your group already has validated Nextflow or Snakemake pipelines that an agent would only be wrapping. It is also the wrong tool when you need results signed off by a process someone outside this project controls, because the validation claim is internal.
Anyone who runs a coding agent next to real experimental work should take this seriously, mainly for the database coverage and the worked examples: those are the parts a general model gets wrong quietly, by inventing an identifier or a plausible method. Treat the 177 skills as a well-organized procedure manual your agent can read, not as a guarantee of correct science, and keep reviewing output the way you would review a new postdoc's first analysis. If your host does not support Agent Skills yet, bookmark it and come back; there is nothing to install until then.