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Showing SKILL.md
| name | swarm-orchestrator |
| description | Run Agent Swarms |
| keywords | ["swarm","orchestration","multi-agent","parallel-execution","routing"] |
| measurable_outcome | Successfully coordinates 3+ specialized agents to resolve complex queries with 100% completion rate. |
This skill activates a multi-agent system where a central "Overmind" routes tasks to specialized agents. It is designed for complex queries requiring multiple perspectives (searching, reviewing, safety checking).
asyncio.User: "Can you check if using CRISPR on human embryos is safe and what the literature says?"
Agent Action:
python3 Skills/Agentic_AI/Multi_Agent_Systems/orchestrator.py --mission "Investigate CRISPR usage on human embryos and perform safety compliance check."
Operate MedSAM2 for promptable segmentation of 3D medical images and medical videos, including CT lesion propagation, MRI volumes, RECIST-guided prompts, efficient CPU-oriented variants, training, and 3D Slicer integration. Use when generating or validating volumetric masks from sparse prompts or propagating masks through image slices or video frames.
Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.
Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.
Based on SOC occupation classification