MCPInstall

research

by weizhenaProductivity & Automation

Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.

Installs
746
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/weizhena/deep-research-skills --skill research
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads research automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Research Skill - Preliminary Research

Trigger

/research <topic>

Workflow

Step 1: Generate Initial Framework from Model Knowledge

Based on topic, use model's existing knowledge to generate:

  • Main research objects/items list in this domain
  • Suggested research field framework

Output {step1_output}, use request_user_input to confirm:

  • Need to add/remove items?
  • Does field framework meet requirements?

Step 2: Web Search Supplement

Use request_user_input to ask for time range (e.g., last 6 months, since 2024, unlimited).

Parameter Retrieval:

  • {topic}: User input research topic
  • {YYYY-MM-DD}: Current date
  • {step1_output}: Complete output from Step 1
  • {time_range}: User specified time range

Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.

Launch 1 web-search-agent (background), Prompt Template:

prompt = f"""## Task
Research topic: {topic}
Current date: {YYYY-MM-DD}

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
{step1_output}

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for {topic} related items within {time_range} and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)
"""

One-shot Example (assuming researching AI Coding History):

## Task
Research topic: AI Coding History
Current date: 2025-12-30

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
### Items List
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
2. Cursor: AI-first IDE, based on VSCode
...

### Field Framework
- Basic Info: name, release_date, company
- Technical Features: underlying_model, context_window
...

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for AI Coding History related items within since 2024 and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)

Step 3: Ask User for Existing Fields

Use request_user_input to ask if user has existing field definition file, if so read and merge.

Step 4: Generate Outline (Separate Files)

Merge {step1_output}, {step2_output} and user's existing fields, generate two files:

outline.yaml (items + config):

  • topic: Research topic
  • items: Research objects list
  • execution:
    • batch_size: Number of parallel agents (confirm with request_user_input)
    • items_per_agent: Items per agent (confirm with request_user_input)
    • output_dir: Results output directory (default: ./results)

fields.yaml (field definitions):

  • Field categories and definitions
  • Each field's name, description, detail_level
  • detail_level hierarchy: brief -> moderate -> detailed
  • uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)

Step 5: Output and Confirm

  • Create directory: ./{topic_slug}/
  • Save: outline.yaml and fields.yaml
  • Show to user for confirmation

Output Path

{current_working_directory}/{topic_slug}/
  ├── outline.yaml    # items list + execution config
  └── fields.yaml     # field definitions

Follow-up Commands

  • /research-add-items - Supplement items
  • /research-add-fields - Supplement fields
  • /research-deep - Start deep research

FAQ

What does the research skill do?

Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.

How do I install research?

Run npx skills add https://github.com/weizhena/deep-research-skills --skill research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/research/ (personal) or .claude/skills/research/ (project).

Which AI agents can use research?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is research safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains research?

It is published by weizhena in weizhena/deep-research-skills. It has 746 recorded installs.

More from weizhena/deep-research-skills

View all →

research-deep

weizhena/deep-research-skills

Read research outline, launch independent agent for each item for deep research. Disable task output.

Productivity & Automation914 installs

research-report

weizhena/deep-research-skills

Summarize deep research results into markdown report, cover all fields, skip uncertain values.

Docs & Writing860 installs

research-add-fields

weizhena/deep-research-skills

Add field definitions to existing research outline.

Productivity & Automation686 installs

research-add-items

weizhena/deep-research-skills

Add items (research objects) to existing research outline.

Productivity & Automation684 installs

Popular productivity & automation skills

View category →

agent-browser

vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

OfficialProductivity & Automation1.3M installs

lark-doc

open.feishu.cn

飞书云文档（Docx / Wiki）内容操作：读取、创建、编辑文档，插入或下载图片附件，以及操作思维笔记。用户提供文档 URL/token（包括 doubao.com 的 /docx/、/wiki/）时使用；按 URL 路径/token 而非域名路由；/page/ 是妙搭应用，非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive；随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。

Productivity & Automation815.7K installs

lark-base

open.feishu.cn

飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。

Productivity & Automation815K installs

lark-drive

open.feishu.cn

飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。

Productivity & Automation815K installs

lark-shared

open.feishu.cn

Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

Productivity & Automation814.8K installs

lark-im

open.feishu.cn

飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。

Productivity & Automation814.1K installs