market-research

name	market-research
description	统一的市场与用户研究入口：市场面收集市场趋势、行业报告并识别机会（TAM-SAM-SOM）；用户面进行用户访谈、调研与画像创建。当用户需要市场研究、行业分析、趋势识别、市场规模分析，或需要了解用户需求、创建用户画像、进行用户访谈，或说"X 市场发生了什么"、"用户想要什么"、"用户研究"时使用。即使没有明确说"市场研究"或"用户研究"，当用户正在研究某个市场或行业，或试图理解用户行为、需求与痛点时也应激活。 Also triggers on: market research, what is happening in the X market, market sizing, what do users want, user research.
layer	perception
input-from	user
output-to	product-strategy,competitive-analysis,prd-gen

Market Research

统一研究入口：市场面看清赛道，用户面看懂用户。

What This Skill Does

按研究交付物分为两种模式：

• 市场面 (Market Intelligence)：搜索驱动的市场趋势、市场规模（TAM-SAM-SOM）、关键玩家、机会与威胁分析，交付 market-analysis.json + Markdown 报告
• 用户面 (User Research)：访谈提纲、调研设计、用户画像与洞察，交付 user-research.json + Markdown 报告

两种模式可独立运行，也可组合执行（先市场面定赛道，再用户面定人群）。

When to Use

市场面 — activate when:

• User asks about market trends or industry developments
• Phrases like "market research", "industry analysis", "market size", "X 市场发生了什么"
• Exploring new markets or product categories
• Need to understand market dynamics before product decisions

用户面 — activate when:

• User asks about understanding user needs or behaviors
• Phrases like "user research", "talk to users", "用户想要什么", "create personas"
• Creating or updating user personas
• Analyzing user feedback or interview data
• Need to validate product ideas with real users

How It Works

1. Determine mode — 根据用户意图选择市场面、用户面或两者组合；意图不明时询问用户
2. 市场面: Define market scope → Search industry sources → Analyze trends → Assess market size (TAM/SAM/SOM) → Identify opportunities → Generate insights
3. 用户面: Define research goals → Select methods → Create instruments (interview guides, surveys) → Gather data → Analyze findings → Create artifacts (personas, journey maps, insights)

Research Modes

市场面 (Market Intelligence)

搜索驱动：所有市场数据必须来自实际搜索，禁止仅凭训练记忆输出（见 Anti-Hallucination Rules）。

分析深度：

Depth	When to Use	What You Get
overview	Quick market check	Market size, 3 trends, 5 key players
standard	Regular analysis (default)	Full sizing, trends, opportunities, threats, recommendations
deep	Strategic decisions	Segmentation analysis, buying patterns, vendor landscape, 10+ recommendations

用户面 (User Research)

研究方法：

Method	When to Use	Output
interviews	Deep understanding of needs	Interview transcripts, key quotes
surveys	Broad pattern validation	Survey results, statistical insights
personas	User archetype creation	Persona profiles
journey-mapping	Experience optimization	User journey maps

【未验证】规则（关键约束）：没有真实用户数据（真实访谈记录、调研结果或用户提供的一手资料）时，不得把推断包装成结论：

• 所有基于推断的画像和结论必须标注【未验证】
• 同时注明所需验证方式（如"需 5+ 目标用户访谈验证"、"需 N≥100 问卷验证"）
• 推断画像可作为假设交付，但必须与真实数据得出的结论明确区分

引用行业报告、统计数据时，同样适用下方 Anti-Hallucination Rules。

Persona Template

### [Name]（verified / 【未验证】）
**Role**: [Job title] at [Company type]

**Goals**:
- [Primary goal]
- [Secondary goal]

**Pain Points**:
- [Key frustration 1]
- [Key frustration 2]

**Behaviors**:
- [Observable behavior 1]
- [Observable behavior 2]

**Quote**: *"[Direct user quote]"*（无真实引用时删除此行，禁止虚构）

**Demographics**:
- Company size: [X-Y employees]
- Tech stack: [Key tools]
- Experience level: [Years]

**Verification**: [已验证来源 / 所需验证方式]

Interview Guide Template

## Interview Questions

### Warm-up
1. Tell me about your role
2. What does a typical day look like?

### Discovery
3. How do you currently [solve problem X]?
4. What tools do you use?
5. What works well? What doesn't?

### Deep Dive
6. Walk me through the last time you [encountered problem]
7. What was frustrating about that?
8. What did you try instead?

### Closing
9. If you could change one thing, what would it be?
10. Is there anything else I should know?

Anti-Hallucination Rules (Self-Contained)

The following rules are mandatory for this skill. They are inlined here for standalone installation compatibility.

1. Mandatory Search First

输出任何市场数据前必须先使用 WebSearch/WebReader。

• 禁止仅凭训练记忆输出任何具体数据（市场规模、增长率、参与者等）
• 每个数据点必须来自实际搜索和访问的页面
• 搜索查询应包含年份，例如 SaaS project management market size 2025 2026

2. Every Claim Must Have a Source

每个数字、趋势、参与者必须有 URL 来源。

• 没有来源 = 不得写入
• 来源格式：{ "claim": "...", "source": "https://...", "source_name": "...", "fetched_at": "..." }

3. Unknown is Acceptable

找不到数据时标注 "Unknown"，禁止编造。标注未知是诚实，不是失败。

4. Confidence Rating

每个关键数据点标注 confidence (high/medium/low)。

• high: 权威来源（Gartner, Forrester, IDC, 官方财报）
• medium: 可靠来源（TechCrunch, Statista, 行业报告）
• low: 社区讨论、博客、推测性分析

5. Distinguish Fact from Inference

明确区分已验证事实和推断。推断必须标注 basis 和 confidence。

6. Quality Gate (Non-negotiable)

以下任一条未满足，不得输出结果：

• 所有具体数字/趋势声明都有来源 URL
• 无法验证的数据已标注 "Unknown"
• 每个数据点标注了 confidence 等级
• facts 和 inferences 已明确区分
• 搜索记录已附在输出中

Search Record

每个分析输出必须附带搜索记录：{ "search_queries_used": [...], "sources_accessed": [{ "url": "...", "title": "...", "used_for": "..." }] }

Input Parameters

Parameter	Type	Required	Description
mode	string	No	market（市场面）、user（用户面）、both（默认按意图判断）
market	string	市场面必填	Market or industry to analyze (e.g., "SaaS project management")
focus_areas	list	No	Specific areas to focus on (e.g., ["pricing", "enterprise segment"])
geography	string	No	Geographic scope (default: global)
depth	string	No	overview (quick), standard (default), or deep (comprehensive)
research_goal	string	用户面必填	What you want to learn about users
target_segment	string	No	Specific user segment to study
method	string	No	interviews, surveys, personas, all (default)
sample_size	number	No	Target number of participants

Evidence Discipline (证据纪律)

1. 缺失信息标记【待确认】：任何无法从输入或上下文获得的信息，标记为【待确认】，不得脑补填默认值。
2. 证据追溯【未验证】：用户需求/市场断言必须追问证据来源；无证据的标记为【未验证】并注明所需验证方式。
3. 完整度检查门：正式输出交付物前，先输出信息完整度清单（已确认 ✅ / 待确认 ⚠️ / 未验证 ❓），经用户确认后才生成正文。
4. 可追溯：正式输出中的关键结论必须能追溯到以下来源之一：已确认的用户输入、引用的证据（含 URL）、或用户的显式决策；无法追溯的结论视为脑补，删除或改标【待确认】。

Output Structure

每种模式生成两个输出；JSON 文件名为下游契约（contracts/ 与其他 skill 按此引用），不得改名：

1. 市场面 → docs/product/.ompm/market-analysis.json（结构化数据）+ Markdown 报告
2. 用户面 → docs/product/.ompm/user-research.json（结构化数据）+ Markdown 报告

market-analysis.json Format

{
  "analysis": {
    "id": "uuid",
    "timestamp": "2026-03-12T...",
    "market": "SaaS project management",
    "market_size": {
      "tam": { "value": "$10B", "confidence": "high", "source": { "url": "https://...", "source_name": "Gartner", "fetched_at": "2026-03-12T..." } },
      "sam": { "value": "$3B", "confidence": "high", "source"         
                     
    
                   
     
                    
    
     
                      
    
     
             
    
     
                    
    
  
   
     
            
  
   

市场面 Markdown Report Structure

# Market Intelligence Report

## Market Overview
- **Market**: SaaS Project Management
- **Date**: YYYY-MM-DD
- **Geography**: Global

## Market Size
| Segment | Size | Confidence | Source |
|:--------|-----:|:-----------|:-------|
| TAM | $10B | high | Gartner, 2025 |
| SAM | $3B | high | IDC, 2025 |
| SOM | $300M | medium | Internal estimate |

## Key Trends
1. **AI-Powered Automation** - Teams are demanding AI assistance for task management...
2. **Remote Work Collaboration** - Post-pandemic shift continues driving demand...
3. **Industry-Specific Solutions** - Generic tools losing ground to vertical products...

## Key Players
| Company | Market Share | Strength | Weakness |
|:--------|-------------:|:---------|:---------|
| Asana | 20% | Enterprise features | Expensive |
| Monday.com | 18% | UX simplicity | Limited customization |

## Opportunities
- **Small Business Segment**: Underserved by enterprise-focused tools
- **Integration Ecosystem**: Gaps in connecting with specialized tools

## Threats
- Market saturation in mid-market segment
- Platform vendors (Microsoft, Google) adding features

## Strategic Recommendations
1. Focus on small business segment for growth
2. Build deep integrations with developer tools
3. Consider vertical specialization (e.g., for agencies)

user-research.json Format

{
  "research": {
    "id": "uuid",
    "timestamp": "2026-03-12T...",
    "goal": "Understand onboarding pain points",
    "method": "interviews",
    "participants": 8,
    "personas": [
      {
        "name": "Product Manager Alice",
        "role": "PM at B2B SaaS",
        "goals": ["Ship features faster", "Align team"],
        "pain_points": ["Too many tools", "Unclear priorities"],
        "behaviors": ["Checks Jira first thing" 
         
         
         
                
      
      
         
         
         
         
         
      
    
     
               
    
     
             
    
     
      
      
    
  
   
     
            
  
   

用户面 Markdown Report Structure

# User Research Report

## Research Overview
- **Goal**: Understand onboarding pain points
- **Method**: User Interviews
- **Participants**: 8 users
- **Date**: YYYY-MM-DD

## User Personas

### Product Manager Alice（verified）
**Role**: PM at B2B SaaS company (50-200 employees)

**Goals**:
- Ship features faster without breaking things
- Keep team aligned on priorities

**Pain Points**:
- Too many tools to check (Jira, Slack, GitHub, Notion)
- Unclear priorities lead to context-switching

**Behaviors**:
- Checks Jira first thing every morning
- Prefers async communication over meetings

**Quote**: *"I just wish everything was in one place instead of checking 5 different apps."*

**Verification**: 8 场真实访谈交叉验证

### Hypothetical Startup PM【未验证】
**Role**: PM at early-stage startup

**Pain Points**:
- [推断项] 资源有限，一人多角

**Verification**: 基于市场面用户细分推断，需 5+ 目标用户访谈验证

## Key Insights

### Insight 1: Context-Switching Friction
**Finding**: Users spend 40% of time switching between tools
**Evidence**: 6/8 participants mentioned this as top frustration
**Opportunity**: Unified workspace could save hours daily

## Recommendations

1. **Short-term**: Add status page showing all work in one view
2. **Medium-term**: Build integrations with Jira, GitHub, Slack
 : Create AI-powered priority recommendations


 [ ] 验证【未验证】画像：完成 5+ 目标用户访谈
 [ ] Run A/B test on simplified onboarding

Quality Standards

市场面：

• Cover market size (TAM/SAM/SOM) — use "Unknown" if not found, do NOT fabricate
• Identify real trends found through search (no minimum count required)
• List major players found in search results
• Provide actionable opportunities based on real data
• Note significant threats

用户面：

• Include 3+ personas (if applicable) — base on real data, do NOT fabricate
• 无真实用户数据时，所有推断画像/结论已标注【未验证】并注明所需验证方式
• Provide real insights with evidence counts from actual research
• Include direct user quotes with source references（无真实引用则不引用，禁止虚构）
• Link insights to actionable recommendations

通用：

• ALL numeric claims, market sizes, growth rates, industry benchmarks have source URLs
• ALL data points have confidence rating (high/medium/low)
• Facts and inferences are clearly distinguished
• Search record is included proving searches were executed
• 完整度检查门已通过（Evidence Discipline 第 3 条：已确认 ✅ / 待确认 ⚠️ / 未验证 ❓ 清单经用户确认）
• Be valid JSON for downstream skills

Context Integration

Reads:

• docs/product/.ompm/market-analysis.json — 用户面复用市场上下文（组合执行时）

Writes:

• docs/product/.ompm/market-analysis.json — 市场分析结果（契约文件名，供下游 skill 使用）
• docs/product/.ompm/user-research.json — 用户研究结果（契约文件名，供下游 skill 使用）

Read By:

• product-strategy — Uses market data and personas for positioning strategy
• competitive-analysis — References key players for comparison
• prd-gen — References user needs and market opportunities in requirements

Example Usage

User: "What's the market for AI writing assistants?"
→ 市场面：搜索驱动分析该市场，输出 market-analysis.json

User: "Research the project management software market"
→ 市场面，focus on SaaS PM tools

User: "Is the fitness app market growing?"
→ 市场面，depth=overview

User: "Create personas for our product"
→ 用户面，method=personas；无真实数据时画像标注【未验证】并注明验证方式

User: "Analyze these user interviews"
→ 用户面，分析用户提供的访谈数据（真实数据，可直接得出结论）

User: "先研究下市场规模，再帮我做用户画像"
→ 组合执行：市场面 → 用户面

Execution Profile (执行建议)

• 模型建议：轻量(haiku 级)——宿主支持多模型/子代理时按此分配；单模型宿主忽略
• 工具姿态：读写 + 联网搜索——遵循最小权限
• 记忆文件：docs/product/.ompm/memory/market-research.md——跨会话积累经验（优质信源、领域基线、分析框架）；执行开始时读取、结束时更新；文件不存在则新建
• Claude Code 增强：本 profile 在该宿主由 market-researcher subagent 以隔离上下文实现

下一步

本 skill 完成后，如果用户没有明确下一步，引导用户使用 ompm skill 做意图路由——它会读取本轮产出和当前状态，判断最有价值的下一步。不要替用户预设固定长链；output-to 声明的是数据流向，不是强制路径。