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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:

Skill	What It Does
Single-Cell RNA QC	Quality control for scRNA-seq data with MAD-based filtering
scvi-tools	Deep learning for single-cell omics (scVI, scANVI, totalVI, PeakVI, etc.)
Nextflow Pipelines	Run nf-core pipelines (RNA-seq, WGS/WES, ATAC-seq)
Instrument Data Converter	Convert lab instrument output to Allotrope ASM format
Scientific Problem Selection	Systematic 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:

~~genomics platform — Access cloud analysis data and workflows Install: Download
```
txg-node.mcpb
```
from https://github.com/10XGenomics/txg-mcp/releases
~~tool database (Harvard MIMS) — AI tools for scientific discovery Install: Download
```
tooluniverse.mcpb
```
from https://github.com/mims-harvard/ToolUniverse/releases

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：

~~genomics platform — 访问云分析数据与工作流安装：从https://github.com/10XGenomics/txg-mcp/releases下载`txg-node.mcpb`
~~tool database (Harvard MIMS) — 用于科学发现的AI工具安装：从https://github.com/mims-harvard/ToolUniverse/releases下载`tooluniverse.mcpb`

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:

Literature review — "Search ~~literature database for recent papers on [topic]"
Analyze sequencing data — "Run QC on my single-cell data" or "Set up an RNA-seq pipeline"
Drug discovery — "Search ~~chemical database for compounds targeting [protein]" or "Find drug targets for [disease]"
Data standardization — "Convert my instrument data to Allotrope format"
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：

文献综述 — "检索~~文献数据库中关于[主题]的近期论文"
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"
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"
数据标准化 — "将我的仪器数据转换为Allotrope格式"
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。