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MAGE

MAGE,来自 FreedomIntelligence/OpenClaw-Medical-Skills 的 agent 技能。

读凭据执行命令读文件严重 0 · 高危 3FreedomIntelligence/OpenClaw-Medical-Skills

它会碰到什么

扫了多少7 个文本文件,35 KB
它会碰到什么读凭据执行命令读文件
命中总数4 处
命中统计严重 0 · 高 3 · 中 1 · 低 0
逐条看命中(3 条严重或高危)
  • repo/Fine_tuning/full_model_training_24-03-12.py:12cred-envread
    # os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   # see issue #152
  • repo/Fine_tuning/full_model_training_24-03-12.py:13cred-envread
    # os.environ["CUDA_VISIBLE_DEVICES"]= '0,1,2,3'
  • repo/generate_antibodies.py:47exec-spawn
    model.eval()

这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。

技能内容

<!--

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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它属于哪个仓库

星标★ 3,010
本站分层T1
该仓技能数897
原文件路径skills/antibody-design-agent/MAGE/SKILL.md

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