Best use case
Skill: Refua is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Summary
Teams using Skill: Refua should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/refua/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Skill: Refua Compares
| Feature / Agent | Skill: Refua | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Summary
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill: Refua ## Summary Refua is used in drug discovery to computationally fold and score biomolecular complexes (e.g., protein–ligand/protein–protein) and optionally profile ADMET, helping prioritize which molecules to synthesize and test first in a drug discovery pipeline. This skill runs and connects to the **refua-mcp** MCP server, which exposes Refua’s “unified Complex API” as MCP tools for: - **Boltz2** complex folding (+ optional affinity evaluation) - **BoltzGen** design workflows - Optional **ADMET** profiling (when installed) Clawdbot supports MCP natively, so the only requirement is running this MCP server and calling its tools. ([github.com](https://github.com/agentcures/refua-mcp)) --- ## When to use Use this skill when you need to: - Fold a **protein–ligand**, **protein–protein**, or (fold-only) **DNA/RNA** complex - Estimate **binding affinity** for a specified binder within a complex spec - Run **ADMET** predictions for one or more SMILES ligands (if enabled) - Execute GPU/CPU-heavy Refua workflows via MCP tool calls Do NOT use this skill when: - The task is a simple deterministic calculation (prefer a non-ML tool) - The user expects you to invent sequences/SMILES (request inputs instead) - The user requests unsafe wet-lab or clinical guidance --- ## Installation & assets (operator steps) ### 1) Install Refua + refua-mcp Install Refua (CPU or CUDA), then install the MCP server package: ([github.com](https://github.com/agentcures/refua-mcp)) - GPU support: - `pip install refua[cuda]` - CPU-only: - `pip install refua` - MCP server: - `pip install refua-mcp` ### 2) Optional: enable ADMET ADMET tool support is optional and requires an extra: ([github.com](https://github.com/agentcures/refua-mcp)) - `pip install refua[admet]` ### 3) Download model/assets Boltz2 and BoltzGen require model/molecule assets. Refua can download them automatically: ([github.com](https://github.com/agentcures/refua-mcp)) - `python -c "from refua import download_assets; download_assets()"` Default asset locations + overrides: ([github.com](https://github.com/agentcures/refua-mcp)) - **Boltz2** uses `~/.boltz` by default - Override via tool option `boltz.cache_dir` if needed - **BoltzGen** uses a bundled HF artifact by default - Override via tool option `boltzgen.mol_dir` if needed --- ## Running the MCP server Start the server using the module entrypoint: ([github.com](https://github.com/agentcures/refua-mcp)) ```bash python3 -m refua_mcp.server ```
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