/MAGE
<!--
$ npx -y skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill MAGE --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/MAGE
Context preview
The summary Claude sees to decide when to auto-load this skill.
<!--
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 -->
The largest open-source medical AI skill library for OpenClaw.
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