MAGE
MAGE is a monoclonal antibody sequence generator that produces antigen-conditioned antibody sequences in FASTA format based on user-specified parameters. Use this skill when computational antibody design is needed for research or therapeutic development, with results delivered alongside metadata for reproducibility and recommendations for structural validation through AlphaFold or Rosetta.
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/mage && cp -r /tmp/mage/skills/antibody-design-agent/MAGE ~/.claude/skills/mageSKILL.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 -->
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