Data
Skill
/MAGE
<!--
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SKILL.md
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 -->
Read more
<!--
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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Questions about MAGE.
- Which coding agents can use MAGE?
- MAGE installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.