multi-search▌
cat-xierluo/legal-skills · updated Apr 8, 2026
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Parallel multi-topic research with independent agents generating structured knowledge bases from any input material.
- ›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
多主题深度研究技能
概述
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：
- 只做信息检索、归纳与专业表述转化
- 不新增事实，不虚构信息
- 聚焦单一问题，精简输出，能够解决问题即可
- 通用设计，可适用于法律、商业、技术、学术等各领域
触发条件
使用 /multi-search 命令触发，或当用户请求：
- 深度研究多个相关课题
- 进行系统性信息检索
- 整合多角度分析
- 需要生成结构化研究报告
输入格式
方式一：基于文件
/multi-search @文档路径.md
方式二：直接粘贴
/multi-search
[粘贴材料内容]
方式三：指定课题
/multi-search
项目：[项目名称]
研究课题：
1. [课题一]
2. [课题二]
3. [课题三]
处理流程
阶段1：分析准备
- 读取输入材料
- 提取研究课题清单
- 有明确课题：直接使用
- 无明确课题：自动从材料中提取
- 课题拆分原则：
- 方向明确：每个课题对应独特的检索方向
- 避免重叠：确保课题之间检索关键词不重复
- 聚焦问题：每个课题解决一个具体问题
- 确定项目名称和输出位置
阶段2：输出目录智能检测
按优先级检测项目结构：
- 优先检测：
output/目录 → 使用output/[项目名]/ - 其次检测：当前工作目录 → 使用
./[项目名]/ - 兜底方案：用户当前目录 → 使用
./research/
创建目录：[输出目录]/03 - 🔍 深度研究/
阶段3：并行深度研究
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）：
- 项目关键信息（背景、目标、核心问题）
- 完整的课题清单及各课题的检索范围
- 已分配的关键词方向（避免重复的依据）
- 具体需求背景
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：
- 在当前上下文中声明："我将检索 [关键词A, 关键词B] 用于研究 [课题名称]"
- 等待主 Agent 确认无重复后再开始
-
主 Agent 审核：
- 检查该 Agent 声明的关键词是否与已分配方向重复
- 如发现重复，及时通知该 Agent 转向其他方向
-
动态调整：
- 如果某方向已被其他 Agent 覆盖，该 Agent 应转向相关但不同的角度
- 记录调整后的检索方向
深度检索要求：
- 4-6轮深度检索
- 自动选择 WebSearch（搜索发现）或 WebFetch（获取完整内容）
- 关键词差异化，确保每个 Agent 覆盖独特角度
文档生成：
- 聚焦解决单一核心问题
- 简洁明了，能够解决问题即可
- 包含关键来源链接
- 直接可用的结论和建议
阶段4：整合输出
- 生成研究总览文档（000.研究总览.md）
- 整合所有 独立研究 Agent 的核心发现
- 创建文档间导航链接
- 添加综合建议和立即行动清单
输出格式
目录结构
[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
└── ...
总览文档格式
# [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
详细研究文档格式
# [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]
链接规范
核心原则
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）
链接标注约定
- 🔗 → 通用网页资源
- 📚 → 学术文献
- 🏛️ → 机构官网
- 📄 → 数据来源
文档命名规范
编号系统
00.- 研究总览01-09.- 核心研究10-19.- 重要研究20+.- 延伸研究
主题规范
- ✅ 使用简洁中文标题
- ✅ 避免特殊字符
- ✅ 长度15字以内
- ✅ 清晰反映研究对象
质量标准
独立研究 Agent研究质量
- 聚焦单一问题：每个 独立研究 Agent 仅解决一个核心问题
- 检索深度：4-6轮检索（够用即可）
- 精简输出：简洁明了，能够解决问题就行
- 关键引用：引用关键来源（够用即可）
- 直接可用：提供直接可用的结论和建议
文档质量标准
- 结构清晰：章节标题层级分明
- 叙述连贯：段落式叙述，避免过度罗列
- 链接准确：所有链接内嵌到正文相应位置
- 格式统一：遵循统一的格式规范
- 可操作性强：提供具体的步骤、工具、命令
注意事项
严禁行为
- ❌ 不创建二级子目录（如"引用素材/"）
- ❌ 不生成独立的执行总结文件
- ❌ 不使用过度罗列的要点格式
- ❌ 不在文末单独列出引用来源
- ❌ 不添加冗余的进度跟踪章节
推荐做法
- ✅ 使用叙述式段落表达
- ✅ 链接内嵌到相应正文位置
- ✅ 保持简洁的研究总览
- ✅ 提供具体的行动建议
- ✅ 标注清晰的文档编号
依赖
本技能依赖 Claude Code 内置工具，无需额外配置：
- WebSearch：搜索发现
- WebFetch：获取完整内容
- Task：启动独立 独立研究 Agent
变更历史
| 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） |
How to use multi-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add multi-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches multi-search from GitHub repository cat-xierluo/legal-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate multi-search. Access the skill through slash commands (e.g., /multi-search) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★52 reviews- ★★★★★Hana Smith· Dec 20, 2024
I recommend multi-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Henry Abebe· Dec 8, 2024
Solid pick for teams standardizing on skills: multi-search is focused, and the summary matches what you get after install.
- ★★★★★Jin Lopez· Dec 8, 2024
Keeps context tight: multi-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Henry Menon· Nov 27, 2024
We added multi-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Anika Abbas· Nov 11, 2024
multi-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Henry Iyer· Oct 18, 2024
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Soo Anderson· Oct 2, 2024
Registry listing for multi-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Rahul Santra· Sep 21, 2024
We added multi-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Anika Bansal· Sep 21, 2024
Keeps context tight: multi-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Henry Diallo· Sep 13, 2024
multi-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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