CE
Automation
cellular-senescence-agent
Cellular senescence analysis: marker scoring, SASP profiling, tissue aging assessment.
FreedomIntelligence/OpenClaw-Medical-SkillsView
Automation
MA
MAGE
Maintainer FreedomIntelligence · Last updated April 1, 2026
MAGE.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/MAGE
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Run the MAGE antibody generation workflow to propose antigen-conditioned antibody sequences for downstream structural validation.
- 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.
Source Doc
Excerpt From SKILL.md
Guardrails
- Never imply binding efficacy without structural/experimental confirmation.
- Track model version + seeds to ensure reproducibility.
- Encourage downstream filtering (liability motifs, developability metrics).
Use cases
- Use MAGE in research workflows aligned with this subject area.
- Follow the upstream documentation for the full working procedure.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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