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market-researcher市场研究员

Agent Skill

market-researcher 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:market-researcher(市场研究员)
来源仓库:https://github.com/404kidwiz/claude-supercode-skills
仓库路径:skills/market-researcher
安装命令:
npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill market-researcher
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill market-researcher

简介

market-researcher 扮演市场研究员角色,执行定制化调研任务。

  • 适合设计问卷、访谈提纲或二手资料收集路径。
  • 可输出研究方法论与初步结论草稿。
  • 通过 npx skills add 安装,支持 Codex、Claude、Cursor、Gemini CLI 等宿主环境。
  • 建议在使用前明确其是否具备一手数据整理能力。

SKILL.md

Market Researcher

Purpose

Provides comprehensive market research expertise specializing in market sizing, consumer behavior analysis, and strategic opportunity identification. Excels at quantitative market analysis, qualitative consumer insights, and strategic market positioning for business decision-making.

When to Use

  • Sizing markets (TAM/SAM/SOM calculations)
  • Analyzing consumer behavior and purchase decisions
  • Conducting competitive market analysis
  • Identifying market opportunities and white spaces
  • Validating product-market fit or positioning strategies

Quick Start

Invoke this skill when:

  • Sizing markets (TAM/SAM/SOM calculations)
  • Analyzing consumer behavior and purchase decisions
  • Conducting competitive market analysis
  • Identifying market opportunities and white spaces
  • Validating product-market fit or positioning strategies

Do NOT invoke when:

  • Analyzing direct competitors only (use competitive-analyst instead)
  • Pure data analysis without market context (use data-analyst)
  • Sales forecasting from existing data (use data-scientist)
  • Marketing campaign execution (use content-marketer or seo-specialist)


Core Workflows

Workflow 1: Calculate TAM, SAM, SOM

Use case: Sizing addressable market for new product or investment decision

Step 1: Define Market Scope

Market Definition Template:
- Product/Service: [Specific offering]
- Geography: [Target regions]
- Customer Segment: [Who specifically?]
- Time Frame: [Current year or 5-year projection?]

Example:
- Product: AI-powered customer service chatbot for e-commerce
- Geography: United States
- Customer Segment: E-commerce companies with \u003e$10M revenue
- Time Frame: 2024-2029

Step 2: Calculate TAM (Top-Down Approach)

TAM = Total market demand if 100% market share

Data sources:
1. Industry reports (Gartner, Forrester, IBISWorld)
2. Government statistics (Census Bureau, BLS)
3. Trade associations

Example calculation:
Total US e-commerce market: $1.1T (2024)
× % needing customer service: 80%
× Average customer service spend: 2.5% of revenue
TAM = $1.1T × 80% × 2.5% = $22B

Step 3: Calculate SAM (Serviceable Addressable Market)

SAM = Portion of TAM you can realistically serve

Filters to apply:
- Geographic constraints (if only operating in US)
- Product limitations (if only for e-commerce, not all retail)
- Customer size constraints (if targeting $10M+ companies)

Example:
E-commerce companies \u003e$10M revenue: 15,000 companies
× Average annual customer service budget: $500K
SAM = 15,000 × $500K = $7.5B

Step 4: Calculate SOM (Serviceable Obtainable Market)

SOM = Realistic market share you can capture in near term (1-3 years)

Factors:
- Competitive landscape (how many competitors?)
- Your differentiation (unique value prop strength)
- Sales \u0026 marketing capacity (realistic reach)
- Growth trajectory (realistic penetration rate)

Conservative SOM:
Year 1: 0.1-0.5% of SAM
Year 2: 0.5-2% of SAM
Year 3: 1-5% of SAM

Example (Year 3):
SOM = $7.5B × 2% = $150M

Step 5: Bottom-Up Validation

Validate top-down sizing with bottom-up:

Unit Economics Approach:
- Target customers: 15,000 e-commerce companies
- Realistic conversion rate: 5% (industry benchmark)
- Customers acquired: 750
- Average contract value: $50K/year
- Bottom-up market capture: 750 × $50K = $37.5M

Compare: Top-down SOM ($150M) vs Bottom-up ($37.5M)
If gap \u003e3x → revisit assumptions


Workflow 3: Competitive Market Analysis

Use case: Understanding competitive landscape and positioning opportunities

Step 1: Identify Competitors

Competitor Categories:
1. Direct: Same product, same target customer
2. Indirect: Different product, solves same problem
3. Substitute: Alternative way to address need
4. Potential: Could enter market easily

Example (Project Management Software):
- Direct: Asana, Monday.com, ClickUp
- Indirect: Excel/Sheets (for simple tracking)
- Substitute: Consultants (outsource instead of software)
- Potential: Microsoft, Google (have adjacent products)

Step 2: Competitive Intelligence Gathering

Data Sources Matrix:

Public Information:
- Company websites (pricing, features, positioning)
- App store reviews (4.2★ rating, "easy to use" appears 45%)
- Social media (follower count, engagement rate)
- Job postings (hiring for X roles = growing that area)

Industry Sources:
- Gartner Magic Quadrant (market position)
- G2 Crowd reviews (feature comparison, user satisfaction)
- Crunchbase (funding, valuation, investor profiles)
- LinkedIn (employee count trends, key hires)

Competitive Metrics Template:
| Competitor | Pricing | Features | Market Share | Customer Satisfaction |
|------------|---------|----------|--------------|----------------------|
| Asana | $10-25/user/mo | 85% feature parity | ~20% | 4.5/5 (G2) |
| Monday.com | $8-16/user/mo | 90% feature parity | ~15% | 4.6/5 (G2) |

Step 3: Positioning Map

Create 2D positioning map:
X-axis: Price (Low → High)
Y-axis: Feature Complexity (Simple → Advanced)

┌─────────────────────────────────┐
│ Advanced                        │
│                    [Enterprise] │
│                                 │
│  [Our Product]         [Leader] │
│                                 │
│                        [Asana]  │
│  [Budget Option]                │
│ Simple                          │
└─────────────────────────────────┘
  Low Price            High Price

Insight: Gap in "Simple but Premium" quadrant = opportunity


Pattern 2: Van Westendorp Price Sensitivity Analysis

When to use: Determining optimal pricing

Survey Questions (ask in this order):
1. At what price would you consider this product to be so expensive
   that you would not consider buying it? (Too Expensive)

2. At what price would you consider this product to be priced so low
   that you would feel the quality couldn't be very good? (Too Cheap)

3. At what price would you consider this product starting to get
   expensive, so that it is not out of the question, but you would
   have to give some thought to buying it? (Expensive/High Side)

4. At what price would you consider this product to be a bargain—a
   great buy for the money? (Cheap/Good Value)

Analysis:
- Plot cumulative % for each price point
- Optimal Price Point (OPP) = intersection of "Too Expensive" and "Too Cheap"
- Acceptable Price Range = between "Too Cheap" and "Too Expensive" intersections

Example Results:
OPP: $49/month
Range: $35-$75/month
Recommendation: Price at $49-$59 for maximum acceptance


❌ Anti-Pattern 2: Survey Leading Questions

What it looks like:

"Don't you think our innovative new product would solve your problems better than competitors?"

Answer options:
[ ] Yes, absolutely!
[ ] Yes, somewhat
[ ] Maybe

Why it fails:

  • Leading language ("innovative", "better")
  • No negative options (biased toward "yes")
  • Worthless data (everyone says yes)

Correct approach:

"How well does [our product] solve [specific problem] compared to alternatives you've used?"

[ ] Much better
[ ] Somewhat better
[ ] About the same
[ ] Somewhat worse
[ ] Much worse
[ ] Haven't used alternatives


Quality Checklist

Research Design

  • Clear, measurable research objectives defined
  • Sample size calculated for statistical significance
  • Survey/interview questions tested with pilot group
  • No leading or biased questions
  • Mix of qualitative and quantitative methods (if appropriate)

Data Collection

  • Representative sample (demographics match target market)
  • Response rate \u003e25% for surveys (higher is better)
  • Data quality checks during collection
  • Respondent privacy protected (GDPR/CCPA compliant)

Analysis \u0026 Insights

  • Statistical significance tested (p-values, confidence intervals)
  • Outliers identified and handled appropriately
  • Multiple hypotheses tested (not just confirmation bias)
  • Insights validated with multiple data points

Reporting

  • Findings actionable (not just "interesting facts")
  • Visualizations clear and accurate
  • Limitations acknowledged
  • Recommendations prioritized by impact

适合场景

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用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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能力 2

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能力 3

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能力 4

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能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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Claude Code

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Codex

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Gemini CLI

13.23%
按下载量换算400

Cursor

8.61%
按下载量换算261

Antigravity

3.3%
按下载量换算100

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