미세 조정이나 RAG 없음 – Claude Code의 생물정보학을 최대 92% 향상

Turn your AI coding agent into a life sciences expert — 197 skills covering genomics, proteomics, drug discovery, and more. Open source. OmicsHorizon, powered by SciAgent-Skills, achieved 92.0% accuracy on the BixBench-Verified-50 benchmark — outperforming all other systems compared. Notably, Claude Code (Opus 4.6) without skills scores 65.3%, but jumps to 92.0% simply by equipping it with these domain-specific skills (+26.7%p). Want to try these skills without any setup? OmicsHorizon lets you use SciAgent-Skills directly in your browser — just sign up and start analyzing. 197 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, but compatible with any agent that reads markdown skill files. | Category | Skills | Examples | |---|---|---| | Genomics & Bioinformatics | 63 | Scanpy, BioPython, pysam, gget, KEGG, PubMed, scvi-tools | | 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 Biology | 3 | CRISPR sgRNA design, gene expression, cloning | Skill types: 72 toolkits, 53 database connectors, 36 guides, 35 pipelines - Claude Code CLI installed - Git - Python 3.12+ (only needed if you want to run validation scripts) git clone https://github.com/jaechang-hits/SciAgent-Skills.git cd SciAgent-Skills Load SciAgent-Skills as a Claude Code plugin for the current session: claude --plugin-dir /path/to/SciAgent-Skills To verify the plugin loaded, run /plugin inside Claude Code and check that sciagent-skills appears in the Installed tab. Skills become available as /sciagent-skills: : /sciagent-skills:scanpy-scrna-seq /sciagent-skills:rdkit-cheminformatics /sciagent-skills:pymc-bayesian-modeling Or just describe your task — the agent finds the relevant skill automatically: "Perform differential expression analysis on this RNA-seq count matrix" Persistent installation — to load the plugin automatically in every session, use the plugin install command inside Claude Code: /plugin marketplace add jaechang-hits/SciAgent-Skills /plugin install sciagent-skills Clone into your project directory so Claude Code picks up skills via CLAUDE.md : cd your-project git clone https://github.com/jaechang-hits/SciAgent-Skills.git .sciagent-skills Add to your project's CLAUDE.md : ## Scientific Skills Reference skills in `.sciagent-skills/skills/` for domain-specific analysis. Registry: `.sciagent-skills/registry.yaml` cd SciAgent-Skills pixi install Pixi handles the Python environment and all required packages. If you don't have pixi installed: curl -fsSL https://pixi.sh/install.sh | bash Each skill follows a structured template: skills/// SKILL.md # Main skill file (300-550 lines) references/ # Optional deep-dive reference files assets/ # Optional templates, configs A SKILL.md contains: - Frontmatter — name, description, license (for agent discovery) - Overview & When to Use — what the tool does and when to reach for it - Prerequisites — packages, data, environment setup - Quick Start — minimal copy-paste example - Workflow / Core API — step-by-step pipeline or module-by-module API guide - Key Parameters — tunable settings with defaults and ranges - Common Recipes — self-contained snippets for common tasks - Troubleshooting — problem/cause/solution table The agent reads only the description field during planning. Full skill content is loaded on demand when relevant. SciAgent-Skills/ ├── .claude-plugin/ │ └── plugin.json # Claude Code plugin manifest ├── skills/ # All 197 skills organized by category │ ├── genomics-bioinformatics/ │ ├── structural-biology-drug-discovery/ │ ├── scientific-computing/ │ ├── cell-biology/ │ ├── biostatistics/ │ ├── scientific-writing/ │ ├── systems-biology-multiomics/ │ ├── proteomics-protein-engineering/ │ ├── lab-automation/ │ ├── data-visualization/ │ └── molecular-biology/ ├── templates/ # Skill authoring templates ├── registry.yaml # Index of all skills ├── CLAUDE.md # Skill authoring guide └── scripts/ └── validate_registry.py Drug Discovery Pipeline "Search ChEMBL for EGFR inhibitors with IC50 < 100nM, filter with Lipinski rules using RDKit, dock top candidates with AutoDock Vina" Uses: chembl-database-bioactivity → rdkit-chem