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Bio-Research Start

生物研究入门

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
You are helping a biological researcher get oriented with the bio-research plugin. Walk through the following steps in order.
若你看到不熟悉的占位符或需要检查已连接的工具，请查看 CONNECTORS.md。
你将协助一位生物研究人员熟悉生物研究插件，请按以下步骤依次操作。

Step 1: Welcome

步骤1：欢迎语

Display this welcome message:
Bio-Research Plugin

Your AI-powered research assistant for the life sciences. This plugin brings
together literature search, data analysis pipelines,
and scientific strategy — all in one place.
显示以下欢迎信息：
生物研究插件

你的生命科学AI研究助手。该插件集文献检索、数据分析流程
和科研策略于一体，一站式满足你的需求。

Step 2: Check Available MCP Servers

步骤2：检查可用MCP服务器

Test which MCP servers are connected by listing available tools. Group the results:
Literature & Data Sources:
  • ~~literature database — biomedical literature search
  • ~~literature database — preprint access (biology and medicine)
  • ~~journal access — academic publications
  • ~~data repository — collaborative research data (Sage Bionetworks)
Drug Discovery & Clinical:
  • ~~chemical database — bioactive compound database
  • ~~drug target database — drug target discovery platform
  • ClinicalTrials.gov — clinical trial registry
  • ~~clinical data platform — clinical trial site ranking and platform help
Visualization & AI:
  • ~~scientific illustration — create scientific figures and diagrams
  • ~~AI research platform — AI for biology (histopathology, drug discovery)
Report which servers are connected and which are not yet set up.
通过列出可用工具来测试已连接的MCP服务器，并对结果进行分组：
文献与数据源：
  • ~~文献数据库——生物医学文献检索
  • ~~文献数据库——预印本访问（生物学与医学领域）
  • ~~期刊访问——学术出版物获取
  • ~~数据仓库——协作研究数据（Sage Bionetworks）
药物研发与临床：
  • ~~化合物数据库——生物活性化合物数据库
  • ~~药物靶点数据库——药物靶点发现平台
  • ClinicalTrials.gov——临床试验注册库
  • ~~临床数据平台——临床试验站点排名与平台辅助
可视化与AI：
  • ~~科学绘图——创建科学图表与示意图
  • ~~AI研究平台——生物学AI工具（组织病理学、药物研发方向）
汇报哪些服务器已连接，哪些尚未设置。

Step 3: Survey Available Skills

步骤3：调研可用分析技能

List the analysis skills available in this plugin:
SkillWhat It Does
Single-Cell RNA QCQuality control for scRNA-seq data with MAD-based filtering
scvi-toolsDeep learning for single-cell omics (scVI, scANVI, totalVI, PeakVI, etc.)
Nextflow PipelinesRun nf-core pipelines (RNA-seq, WGS/WES, ATAC-seq)
Instrument Data ConverterConvert lab instrument output to Allotrope ASM format
Scientific Problem SelectionSystematic framework for choosing research problems
列出该插件中可用的分析技能：
Skill功能
Single-Cell RNA QC基于MAD过滤的scRNA-seq数据质量控制
scvi-tools单细胞组学深度学习工具（scVI、scANVI、totalVI、PeakVI等）
Nextflow Pipelines运行nf-core流程（RNA-seq、WGS/WES、ATAC-seq）
Instrument Data Converter将实验室仪器输出转换为Allotrope ASM格式
Scientific Problem Selection选择研究课题的系统化框架

Step 4: Optional Setup — Binary MCP Servers

步骤4：可选设置——二进制MCP服务器

Mention that two additional MCP servers are available as separate installations:
These require downloading binary files and are optional.
提及还有两个额外的MCP服务器可单独安装：
这些需要下载二进制文件，属于可选组件。

Step 5: Ask How to Help

步骤5：询问需求

Ask the researcher what they're working on today. Suggest starting points based on common workflows:
  1. Literature review — "Search ~~literature database for recent papers on [topic]"
  2. Analyze sequencing data — "Run QC on my single-cell data" or "Set up an RNA-seq pipeline"
  3. Drug discovery — "Search ~~chemical database for compounds targeting [protein]" or "Find drug targets for [disease]"
  4. Data standardization — "Convert my instrument data to Allotrope format"
  5. Research strategy — "Help me evaluate a new project idea"
Wait for the user's response and guide them to the appropriate tools and skills.
询问研究人员当前的工作内容。根据常见工作流提供起始建议：
  1. 文献综述 — "检索~~文献数据库中关于[主题]的近期论文"
  2. 测序数据分析 — "对我的单细胞数据进行QC检查" 或 "搭建RNA-seq分析流程"
  3. 药物研发 — "在~~化合物数据库中检索靶向[蛋白质]的化合物" 或 "寻找针对[疾病]的药物靶点"
  4. 数据标准化 — "将我的仪器数据转换为Allotrope格式"
  5. 科研策略 — "帮我评估一个新的项目想法"
等待用户回复并引导他们使用合适的工具与技能。