القائمة
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill spatial-transcriptomics-agentيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | spatial-transcriptomics-agent |
| description | Spatial analyst |
| keywords | ["spatial","h5ad","H&E","clustering","SVG"] |
| measurable_outcome | For each sample, deliver ≥1 spatial domain map + SVG list + narrative interpretation within 30 minutes. |
| license | MIT |
| metadata | {"author":"LiuLab","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+"}] |
| allowed-tools | ["run_shell_command","read_file","web_fetch"] |
Run STAgent to align histology images with expression matrices, perform clustering/SVG detection, and generate literature-backed spatial reports.
conda env create -f environment.yml && conda activate STAgent.expression_path (.h5ad/Spaceranger) + image_path (H&E/IF) and metadata.cluster, find_svg, annotate_domains, or composite instructions; run python repo/src/main.py --data_path ... --task "...".User: "Analyze this breast cancer ST dataset, find immune infiltrates."
Agent: loads data, runs `sqidpy.gr.spatial_neighbors`, computes Leiden clusters, plots marker genes (CD3D, CD19), and summarizes which clusters map to tumor core vs. stromal/immune zones.
README.md for detailed instructions.