Parallel multi-topic research with independent agents generating structured knowledge bases from any input material.
Works with
Launches dedicated research agents for each topic in parallel, each conducting 4–6 rounds of deep retrieval with deduplication checks to avoid keyword overlap
Accepts three input modes: file-based ( /multi-search @path.md ), direct paste, or explicit topic specification with custom research directions
Generates structured output with a research overview document, indiv
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmulti-searchExecute the skills CLI command in your project's root directory to begin installation:
Fetches multi-search from cat-xierluo/legal-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate multi-search. Access via /multi-search in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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智能多主题深度研究工具，自动分析材料并生成系统化研究文档。支持任意材料输入，通过并行启动多个独立研究 Agent进行深度检索，形成精简的研究知识库。
核心原则：
使用 /multi-search 命令触发，或当用户请求：
/multi-search @文档路径.md
/multi-search
[粘贴材料内容]
/multi-search
项目：[项目名称]
研究课题：
1. [课题一]
2. [课题二]
3. [课题三]
按优先级检测项目结构：
output/ 目录 → 使用 output/[项目名]/./[项目名]/./research/创建目录：[输出目录]/03 - 🔍 深度研究/
为每个研究课题启动独立的 general-purpose 独立研究 Agent。
上下文传递（主Agent → 独立研究 Agent）：
去重检查机制：
每个 独立研究 Agent 在开始检索前，必须遵循以下流程：
检索前声明：
主 Agent 审核：
动态调整：
深度检索要求：
文档生成：
[输出目录]/
└── [项目名]/
└── 03 - 🔍 深度研究/
├── 000.研究总览.md
├── YYMMDD [研究课题一].md
├── YYMMDD [研究课题二].md
└── ...
# [项目名称] 深度研究总览
**生成时间**: YYYY-MM-DD
**研究方式**: N个独立研究 Agent，各进行4-6轮深度检索
**总检索轮次**: XX+轮
**总文档量**: XX KB
---
## 研究成果清单
### 已完成的N份精简研究报告
| 序号 | 研究课题 | 文件大小 | 核心价值 |
|------|---------|---------|---------|
| 01 | [课题一](./YYMMDD%20课题一.md) | XX KB | 简要描述 |
---
## 核心发现
### 发现1：[最重要发现]
**依据**：[简要说明]
**结论**：[具体结论]
---
## 综合建议
### 一、策略建议
**推荐方案**：[具体方案]
### 二、立即行动清单
- [ ] 行动项1
- [ ] 行动项2
# [研究课题标题]
**生成时间**: YYYY-MM-DD
**研究深度**: XX+轮深度检索，覆盖XXXX、XXXX、XXXX
---
## 核心结论
[最重要的发现和结论，2-3段，充分详实]
---
## 一、[主要内容一]
### （一）子标题
正文段落。引用来源使用内嵌链接格式：
- 根据[来源名称](https://链接)...
- 依据[资料](https://链接)...
---
## 二、[主要内容二]
[继续结构化内容]
---
## 三、应用建议
### （一）建议要点
**内容**：[具体内容]
### （二）注意事项
⚠️ [注意点]
所有来源链接必须内嵌到正文中相应位置
✅ 正确：
根据[研究报告](https://链接)显示...
❌ 错误：
根据某报告...
（文末单独列出引用来源）
00. - 研究总览01-09. - 核心研究10-19. - 重要研究20+. - 延伸研究本技能依赖 Claude Code 内置工具，无需额外配置：
| 版本 | 日期 | 更新内容 |
|---|---|---|
| v1.0.0 | 2026-02-15 | 从 Command 迁移为 Skill，重命名为多主题深度研究（multi-search） |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
kostja94/marketing-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
I recommend multi-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: multi-search is focused, and the summary matches what you get after install.
Keeps context tight: multi-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added multi-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
multi-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for multi-search matched our evaluation — installs cleanly and behaves as described in the markdown.
We added multi-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: multi-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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