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auto-proteomics自动蛋白质组学

Agent Skill

auto-proteomics 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,575

周安装

152

GitHub Stars

公开资料未说明

下载量

1,252
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:auto-proteomics(自动蛋白质组学)
来源仓库:https://github.com/billwanttobetop/auto-proteomics
安装命令:
openclaw skills install auto-proteomics
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install auto-proteomics

简介

auto-proteomics 用于处理 DDA LFQ 蛋白质组输入的低令牌路由和下游分析。

  • 适合在 OpenClaw 中已有蛋白质水平定量数据时进行后续分析处理。
  • 可辅助完成蛋白质组学相关的数据路由和分析任务,提升专业领域能力。
  • 安装命令:openclaw skills install auto-proteomics;需确认权限范围和维护状态。
  • 注意是否会触发联网、命令执行或文件读写操作,建议结合原始 README 核验具体用法。

SKILL.md

name
auto-proteomics
description
Public OpenClaw skill for low-token routing and downstream analysis of processed DDA LFQ proteomics inputs. Use when the user already has protein-level quantification tables such as MaxQuant-style proteinGroups.txt and needs a clear two-group downstream workflow.

Auto Proteomics

Author: Guo Xuan 郭轩 Contact: xguo608@connect.hkust-gz.edu.cn

auto-proteomics is a public v0.x skill for processed proteomics downstream work.

The current public promise is intentionally narrow:

  • one shipped runnable workflow: dda-lfq-processed
  • one public input family: processed DDA LFQ protein-level tables
  • one public comparison model: group-a vs group-b

Everything else in this repository should be read as routing context, internal prototype, or future scaffold unless a document explicitly marks it as part of the public promise. Presence of a script, schema, or branch document does not mean the route is publicly supported. In particular, dia-quant is intentionally exposed as an internal prototype route for correct routing and contract validation, not as a shipped public workflow.

Use this skill when

  • the user already has processed protein-level quantification output
  • the main table is MaxQuant-like proteinGroups.txt
  • the goal is QC, normalized matrix generation, and two-group differential protein analysis
  • the user wants a low-token, file-driven workflow instead of a long chat-only protocol

Do not use this skill when

  • the user starts from raw spectra and needs search/identification
  • the request is primarily DIA, phosphoproteomics, enrichment, or multi-omics execution
  • the task requires more than one comparison design in the current release
  • the user only wants generic statistics with no proteomics context

Public promise in v0.x

Shipped and supported now:

  • route processed DDA LFQ downstream requests into dda-lfq-processed
  • validate the expected processed-input shape
  • generate matrix, QC, differential tables, report, and manifest outputs

Not promised yet:

  • raw-spectrum search pipelines
  • DIA public execution support
  • phosphoproteomics execution
  • enrichment execution
  • multi-omics execution
  • generalized study-design handling beyond the current two-group path

Internal prototype route available for routing only:

  • dia-quant may be selected only when the request is explicitly about processed DIA quant tables that fit the checked-in DIA contract
  • selecting dia-quant means internal prototype triage, never a public v0.x execution recommendation

Important boundary:

  • non-shipped branches may contain scaffold or prototype execution files for internal framework development
  • smaller models must not treat those files as public runnable recommendations unless a route is explicitly marked shipped

Minimal workflow

  1. Read references/WORKFLOW_INDEX.yaml
  2. If the route is unclear, run scripts/decision/route_proteomics.py
  3. Check that the request fits the public v0.x boundary
  4. Run scripts/workflows/dda_lfq_processed.sh
  5. Use references/ for runtime, onboarding, and development rules

Public runnable entrypoint

bash scripts/workflows/dda_lfq_processed.sh \
  --input-dir <run_dir> \
  --protein-groups <proteinGroups.txt> \
  --summary <summary.txt> \
  --parameters <parameters.txt> \
  --output-dir <output_dir> \
  --group-a <condition_a> \
  --group-b <condition_b>

Input contract

Required:

  • proteinGroups.txt with LFQ intensity * or Intensity * columns
  • summary.txt with Raw file and Experiment columns

Optional:

  • parameters.txt

Output contract

The shipped workflow produces:

  • normalized protein matrix files under matrix/
  • QC outputs under qc/
  • differential protein tables under stats/
  • REPORT.md
  • summary.json
  • run_manifest.json

Repository layers

  • SKILL.md: public entry and release boundary
  • references/WORKFLOW_INDEX.yaml: machine-readable routing and shipped-vs-non-shipped map
  • references/BRANCH_FRAMEWORK.md: standard branch contract for future routes
  • references/branches/: per-branch specs for scaffold and prototype workflows
  • references/DIA_INPUT_SCHEMA.md: first narrow schema for DIA prototype intake
  • scripts/workflows/dda_lfq_processed.sh: shipped workflow entrypoint
  • shipped public guidance lives in documents that explicitly describe the processed DDA v0.x path
  • non-shipped reference docs exist for internal framework development and must not be surfaced as public support

Read next

  • references/WORKFLOW_INDEX.yaml
  • references/RUNTIME_REQUIREMENTS.md
  • references/BRANCH_FRAMEWORK.md
  • references/DEMO_INPUT_GUIDE.md
  • references/DEVELOPMENT_GUIDE.md

适合场景

01

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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按下载量换算1,045

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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来源信息

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