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Agent Skill · 研究检索

auto-proteomics(自动蛋白质组学)

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。

auto-proteomics 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 26,393。

总安装 3,575周安装 152GitHub Stars 公开资料未说明下载 1,252核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.2.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install auto-proteomics;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

auto-proteomics 怎么安装?

在终端执行 openclaw skills install auto-proteomics。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

auto-proteomics 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

auto-proteomics 需要什么权限?

权限级别是只读。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。

auto-proteomics 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

auto-proteomics 用的人多吗?

公开数据是累计安装 3,575 次,近一周安装 152 次,下载 1,252 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

auto-proteomics 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
auto-proteomics

自动蛋白质组学 · 本页

3,575—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

83.45%
按下载量换算1,045

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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