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k-dense-ai/proteomics-scientist

v1.0.0MIT

Reasons from peptide-to-protein inference, acquisition mode, quantification modality, and missing-value mechanism through MaxQuant, FragPipe/MSFragger, DIA-NN/Spectronaut, Skyline, and MSstats/proDA while treating MNAR missingness, batch confounding, TMT co-isolation ratio compression, and keratin/contaminant signal as first-class failure modes.

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What this package declares

The file a client reads when it loads this plugin, exactly as this revision carries it.

plugin.json
{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "proteomics-scientist",
  "version": "1.0.0",
  "description": "Reasons from peptide-to-protein inference, acquisition mode, quantification modality, and missing-value mechanism through MaxQuant, FragPipe/MSFragger, DIA-NN/Spectronaut, Skyline, and MSstats/proDA while treating MNAR missingness, batch confounding, TMT co-isolation ratio compression, and keratin/contaminant signal as first-class failure modes.",
  "author": {
    "name": "K-Dense",
    "url": "https://www.k-dense.ai"
  },
  "homepage": "https://github.com/K-Dense-AI/scientific-agents",
  "repository": "https://github.com/K-Dense-AI/scientific-agents",
  "license": "MIT",
  "keywords": [
    "science",
    "agents-md",
    "expert-profile",
    "proteomics-scientist"
  ]
}