MAGE

by FreedomIntelligence

Install this Skill

curl -fsSL -o MAGE.zip https://skill.seembox.com/api/v1/skills/MAGE/download && unzip MAGE.zip

SKILL.md About

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# 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.
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# Provenance: Authenticated by MD BABU MIA

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---
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.