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npx versuz@latest install freedomintelligence-openclaw-medical-skills-skills-antibody-design-agent-magegit clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.gitcp OpenClaw-Medical-Skills/SKILL.MD ~/.claude/skills/freedomintelligence-openclaw-medical-skills-skills-antibody-design-agent-mage/SKILL.md<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA --> --- name: mage-antibody-generator description: Ab seq forge keywords: - antibody - antigen - FASTA - generation - validation measurable_outcome: Generate the requested number of antibody sequences (default ≥5) with metadata (model checkpoint, seed) and deliver FASTA files within 10 minutes. license: MIT metadata: author: MAGE Team version: "1.0.0" compatibility: - system: Python 3.9+ / GPU allowed-tools: - run_shell_command - read_file --- # MAGE (Monoclonal Antibody Generator) Run the MAGE antibody generation workflow to propose antigen-conditioned antibody sequences for downstream structural validation. ## Workflow 1. **Prep env:** `cd repo` and install dependencies, then point to GPU if available. 2. **Run generator:** `python generate_antibodies.py --antigen_sequence <SEQ> --num_candidates N --output_dir ./results`. 3. **Collect outputs:** Provide FASTA paths + metadata, optionally translate into JSON manifest. 4. **Recommend validation:** Suggest AlphaFold/Rosetta checks and wet-lab follow-up. ## Guardrails - Never imply binding efficacy without structural/experimental confirmation. - Track model version + seeds to ensure reproducibility. - Encourage downstream filtering (liability motifs, developability metrics). ## References - Source instructions in `README.md` and repo scripts. <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->