Token导航 LogoToken导航

Agent Skill · 研究检索

market-researcher(市场研究员)

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。

market-researcher 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 13,162。

总安装 9,641周安装 390GitHub Stars 76下载 3,026核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/market-researcher

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill mark…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

market-researcher 怎么安装?

在终端执行 npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill market-researc…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

market-researcher 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

market-researcher 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

market-researcher 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

market-researcher 用的人多吗?

公开数据是累计安装 9,641 次,近一周安装 390 次,下载 3,026 次,GitHub 76 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

market-researcher 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
market-researcher

市场研究员 · 本页

9,64176权限未标注MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

Claude Code

32.22%
按下载量换算975

OpenCode

21.16%
按下载量换算640

Codex

19.67%
按下载量换算595

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

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

继续浏览同类 Skills