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Showing SKILL.md
| name | leads-literature-mining |
| description | Review Automator |
| keywords | ["literature-mining","systematic-review","meta-analysis","pubmed","evidence-synthesis"] |
| measurable_outcome | Complete a systematic review screen of 100+ papers with >90% inclusion/exclusion accuracy compared to human baseline. |
| license | CC-BY-4.0 |
| metadata | {"author":"Nature Communications 2025","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+"}] |
| allowed-tools | ["run_shell_command","web_fetch"] |
A specialized LLM agent for automating systematic reviews and meta-analyses, capable of high-accuracy study selection and data extraction.
User: "Perform a systematic review on the efficacy of CAR-T in solid tumors."
Agent Action:
python -m leads.review --topic "CAR-T solid tumors" --criteria ./criteria.json
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