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Database search for peptide/protein identification using MaxQuant, MS-GF+, Comet, or Mascot.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill proteomics-identification命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | proteomics-identification |
| description | Database search for peptide/protein identification using MaxQuant, MS-GF+, Comet, or Mascot. |
| version | 0.1.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["proteomics","peptide-identification","MaxQuant","MS-GF+","Comet"] |
| metadata | {"omicsclaw":{"domain":"proteomics","emoji":"🔬","trigger_keywords":["peptide identification","database search","MaxQuant","MS-GF+","Comet","Mascot"],"allowed_extra_flags":[],"legacy_aliases":["peptide-id"],"saves_h5ad":false}} |
Peptide and protein identification from MS/MS spectra. Wraps MaxQuant/Andromeda, MS-GF+, and Comet.
python omicsclaw.py run peptide-id --demo
python omicsclaw.py run peptide-id --input <spectra.mzml> --output <dir>
output_directory/
├── report.md
├── result.json
├── identified.csv
├── figures/
│ └── fdr_distribution.png
├── tables/
│ └── peptide_evidence.csv
└── reproducibility/
├── commands.sh
├── requirements.txt
└── checksums.sha256
Trigger conditions:
Chaining partners:
ms-qc — Upstream quality checksquantification — Downstream quantitative aggregationOperate 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.
基于 SOC 职业分类
