Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
197 bioinformatics & life science skills for Claude Code and AI agents — BixBench 92.0% accuracy. RNA-seq, single-cell, drug discovery, proteomics, and more. Powers OmicsHorizon.
| Date | Stars |
|---|---|
| 2026-07-24 | 282 |
| 2026-07-25 | 283 |
| 2026-07-28 | 283 |
| 2026-07-30 | 283 |
| 2026-07-31 | 291 |
| 2026-08-06 | 291 |
Today
— stars today
This week
+8 stars this week
This month
— stars this month
Momentum
8.0
growth rate 2.83%/day
# SciAgent-Skills <p align="center"> <img src="https://img.shields.io/badge/skills-199-blue?style=for-the-badge" alt="199 Skills"> <img src="https://img.shields.io/badge/BixBench-92.0%25-brightgreen?style=for-the-badge" alt="BixBench 92.0%"> <img src="https://img.shields.io/badge/license-CC--BY--4.0-lightgrey?style=for-the-badge" alt="CC-BY-4.0"> <img src="https://img.shields.io/github/stars/jaechang-hits/SciAgent-Skills?style=for-the-badge" alt="GitHub Stars"> </p> > **Turn your AI coding agent into a life sciences expert** — 199 bioinformatics skills for Claude Code covering RNA-seq, single-cell analysis, genomics, proteomics, drug discovery, and more. Boosted [BixBench](https://github.com/Future-House/BixBench) from 65% to 92%. Open source. **SciAgent-Skills** is the largest open-source skill library for scientific AI agents. It equips [Claude Code](https://docs.anthropic.com/en/docs/claude-code) (and any markdown-compatible agent) with domain-specific knowledge for computational biology, bioinformatics, cheminformatics, and biostatistics — no fine-tuning required, just plug in and analyze. **Keywords:** bioinformatics AI agent, Claude Code skills, scientific computing, RNA-seq analysis, single-cell RNA-seq, drug discovery pipeline, protein structure prediction, computational biology tools, life science automation, BixBench benchmark ## Benchmark: 92.0% on BixBench-Verified-50 <p align="center"> <img src="assets/benchmark.png" alt="BixBench bioinformatics benchmark results — SciAgent-Skills achieves 92.0% accuracy" width="700"> </p> [BixBench](https://github.com/Future-House/BixBench) is a benchmark for evaluating AI agents on real-world bioinformatics tasks. **SciAgent-Skills achieved 92.0% accuracy** on BixBench-Verified-50, the highest among all tested systems: | System | BixBench-Verified-50 Accuracy | |--------|:----------------------------:| | Claude Code (Opus 4.6) **+ SciAgent-Skills** | **92.0%** | | Claude Code (Opus 4.6) baseline | 65.3% | Simply equipping Claude Code with these domain-specific skills yields a **+26.7 percentage point improvement** — no fine-tuning, no custom model, just structured scientific knowledge. ## Try It Now — OmicsHorizon Want to try these skills without any setup? **[OmicsHorizon](https://omicshorizon.ai/en/)** (오믹스 호라이즌) is the web platform powered by SciAgent-Skills. Sign up and start analyzing your bioinformatics data directly in your browser — RNA-seq, proteomics, drug screening, and more. [](https://omicshorizon.ai/en/) --- **199 ready-to-use scientific skills for AI coding agents** — covering genomics, proteomics, drug discovery, biostatistics, scientific computing, and scientific writing. Each skill is a self-contained SKILL.md file with runnable code examples, key parameters, troubleshooting guides, and best practices. Designed for [Claude Code](https://docs.anthropic.com/en/docs/claude-code), but compatible with any AI agent that reads markdown skill files ([setup guides below](#using-with-other-agents)). ## What's Inside | Category | Skills | Examples | |----------|:------:|----------| | Genomics & Bioinformatics | 65 | Scanpy, BioPython, pysam, gget, KEGG, PubMed, scvi-tools, Bakta, Roary | | Structural Biology & Drug Discovery | 26 | RDKit, AutoDock Vina, ChEMBL, PDB, DeepChem, datamol | | Scientific Computing | 24 | Polars, Dask, NetworkX, SymPy, UMAP, PyG, Zarr, SimPy | | Cell Biology | 15 | pydicom, histolab, FlowIO | | Biostatistics | 12 | scikit-learn, statsmodels, PyMC, SHAP, survival analysis | | Scientific Writing | 21 | Manuscript writing, peer review, LaTeX posters, slides, figure guides | | Systems Biology & Multi-omics | 11 | COBRApy, LaminDB, Reactome, STRING | | Proteomics & Protein Engineering | 10 | ESM, UniProt, PyOpenMS, matchms, HMDB | | Lab Automation | 6 | Opentrons, Benchling | | Data Visualization | 5 | Plotly, Seaborn | | Molecular
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e675e85b947185f9, topic:bioinformatics, topic:scientific-computing, topic:drug-discovery