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<objective> Comprehensive research framework combining market intelligence (company profiles, competitive analysis, tech stack discovery) and technical evaluation (framework comparisons, LLM selection, API assessment). Produces structured reports with confidence scores and actionable recommendations. </objective>

<quick_start> Market research:

Basic discovery: Website, LinkedIn, Google News
Tech stack: Job postings, integrations page
Pain signals: Reviews, social mentions
Decision makers: LinkedIn, about page

Technical research:

Define: Problem, requirements, constraints
Discover: GitHub, HuggingFace, Context7 docs
Evaluate: Apply framework checklist, test minimal example
Decide: Build vs buy, document rationale

Output: Research report with question, answer, confidence, sources </quick_start>

<success_criteria> Research is successful when:

Question clearly defined with constraints documented
Multiple sources consulted (not just one)
Confidence level assigned (high/medium/low) with rationale
Recommendations are specific and actionable
Decision matrix used for multi-option comparisons
NO OPENAI constraint respected for technical research
Sources documented with access dates </success_criteria>

<core_content> Comprehensive research framework combining market intelligence and technical evaluation.

<objective> 一个结合市场情报（企业档案、竞品分析、技术栈挖掘）与技术评估（框架对比、LLM选型、API评估）的综合研究框架。可生成带有置信度评分和可落地建议的结构化报告。 </objective>

<quick_start> 市场调研：

基础信息挖掘：官网、LinkedIn、谷歌新闻
技术栈分析：招聘启事、集成页面
痛点信号收集：用户评价、社交平台提及内容
决策者定位：LinkedIn、企业关于我们页面

技术调研：

明确需求：待解决问题、需求要求、约束条件
资源挖掘：GitHub、HuggingFace、Context7文档
评估验证：应用框架检查清单、测试最小示例
决策输出：自研vs采购、记录决策依据

输出成果： 包含问题、答案、置信度、信息来源的研究报告 </quick_start>

<success_criteria> 满足以下条件时，调研视为成功：

问题定义清晰，约束条件已记录
参考了多来源信息（而非单一来源）
已分配置信度等级（高/中/低）并说明依据
建议具体且可落地
多选项对比时使用了决策矩阵
技术调研严格遵守禁用OpenAI的限制
信息来源已记录并标注访问日期 </success_criteria>

<core_content> 结合市场情报与技术评估的综合研究框架。

Quick Reference

快速参考

Research Type	Output	When to Use	Reference
Company Profile	Structured profile	Before outreach, call prep	`reference/market.md`
Competitive Intel	Market position, pricing	Deal strategy	`reference/market.md`
Tech Stack Discovery	Software + integrations	Lead qualification	`reference/market.md`
Framework Evaluation	Feature comparison + rec	Tech decisions	`reference/technical.md`
LLM Comparison	Cost/capability matrix	Provider selection	`reference/technical.md`
API Assessment	Limits, pricing, DX	Integration planning	`reference/technical.md`
MCP Discovery	Available servers/tools	Capability expansion	`reference/technical.md`

调研类型	输出成果	适用场景	参考文档
企业档案	结构化档案	客户触达前、沟通准备阶段	`reference/market.md`
竞品情报	市场定位、定价策略	交易策略制定	`reference/market.md`
技术栈挖掘	软件及集成信息	销售线索资质判定	`reference/market.md`
框架评估	功能对比及建议	技术决策	`reference/technical.md`
LLM对比	成本/能力矩阵	供应商选型	`reference/technical.md`
API评估	限制条件、定价、开发者体验（DX）	集成规划	`reference/technical.md`
MCP能力挖掘	可用服务器/工具	能力扩展	`reference/technical.md`

Part 1: Market Research

第一部分：市场调研

Company Profile Framework

企业档案框架

python

company_profile = {
    # Basics
    'name': str,
    'website': str,
    'industry': str,
    'employee_count': int,
    'revenue_estimate': str,  # "$5-10M", "$10-50M"

    # Operations
    'field_vs_office': {'field': int, 'office': int},
    'service_area': list[str],  # States/regions
    'trades': list[str],  # Electrical, HVAC, Plumbing

    # Technology
    'software_stack': {
        'crm': str,
        'project_mgmt': str,
        'accounting': str,
        'field_service': str,
        'other': list[str]
    },

    # Sales Intel
    'pain_signals': list[str],
    'growth_indicators': list[str],
    'failed_implementations': list[str],
    'decision_makers': list[dict]
}

python

company_profile = {
    # Basics
    'name': str,
    'website': str,
    'industry': str,
    'employee_count': int,
    'revenue_estimate': str,  # "$5-10M", "$10-50M"

    # Operations
    'field_vs_office': {'field': int, 'office': int},
    'service_area': list[str],  # States/regions
    'trades': list[str],  # Electrical, HVAC, Plumbing

    # Technology
    'software_stack': {
        'crm': str,
        'project_mgmt': str,
        'accounting': str,
        'field_service': str,
        'other': list[str]
    },

    # Sales Intel
    'pain_signals': list[str],
    'growth_indicators': list[str],
    'failed_implementations': list[str],
    'decision_makers': list[dict]
}

Pain Signal Detection

痛点信号识别

Signal	Indicates	Priority
Multiple systems mentioned	Integration pain	HIGH
"Growing fast" in news	Scaling challenges	HIGH
Recent leadership change	Open to new vendors	MEDIUM
Hiring ops/admin roles	Process problems	MEDIUM
Bad software reviews	Ready to switch	HIGH
No online presence	Not tech-savvy	LOW

信号类型	潜在含义	优先级
提及多套系统	集成痛点	高
新闻中提到“快速增长”	规模化挑战	高
近期管理层变动	对新供应商持开放态度	中
招聘运营/行政岗位	流程问题	中
软件差评	有更换意愿	高
无线上存在感	对技术不敏感	低

Market Research Workflow

市场调研工作流

Step 1: Basic Discovery
└── Website, LinkedIn, Google News, Glassdoor

Step 2: Tech Stack
└── Job postings, integrations page, case studies

Step 3: Pain Signals
└── Reviews, social mentions, forum posts

Step 4: Decision Makers
└── LinkedIn Sales Nav, company about page

Step 5: Synthesize
└── Generate company profile, score against ICP

Step 1: 基础信息挖掘
└── 官网、LinkedIn、谷歌新闻、Glassdoor

Step 2: 技术栈分析
└── 招聘启事、集成页面、案例研究

Step 3: 痛点信号收集
└── 用户评价、社交平台提及内容、论坛帖子

Step 4: 决策者定位
└── LinkedIn Sales Nav、企业关于我们页面

Step 5: 信息整合
└── 生成企业档案，根据理想客户画像（ICP）评分

Competitive Positioning

竞品定位

When researching competitors for a prospect:

1. What are they using now?
2. How long have they used it?
3. What's broken? (Check reviews, Reddit, forums)
4. What would make them switch?
5. Who else are they evaluating?

针对潜在客户调研竞品时：

1. 他们当前使用什么产品？
2. 已使用时长？
3. 存在哪些问题？（查看评价、Reddit、论坛）
4. 什么因素会促使他们更换？
5. 他们还在评估哪些竞品？

Part 2: Technical Research

第二部分：技术调研

Stack Constraints (Tim's Environment)

技术栈约束（Tim的环境）

yaml

constraints:
  llm_providers:
    preferred:
      - anthropic  # Claude - primary
      - google     # Gemini - multimodal
      - openrouter # DeepSeek, Qwen, Yi - cost optimization
    forbidden:
      - openai     # NO OpenAI

  infrastructure:
    compute: runpod_serverless
    database: supabase
    hosting: vercel
    local: ollama  # M1 Mac compatible

  frameworks:
    preferred:
      - langgraph  # Over langchain
      - fastmcp    # For MCP servers
      - pydantic   # Data validation
    avoid:
      - langchain  # Too abstracted
      - autogen    # Complexity

  development:
    machine: m1_mac
    ide: cursor, claude_code
    version_control: github

yaml

constraints:
  llm_providers:
    preferred:
      - anthropic  # Claude - primary
      - google     # Gemini - multimodal
      - openrouter # DeepSeek, Qwen, Yi - cost optimization
    forbidden:
      - openai     # NO OpenAI

  infrastructure:
    compute: runpod_serverless
    database: supabase
    hosting: vercel
    local: ollama  # M1 Mac compatible

  frameworks:
    preferred:
      - langgraph  # Over langchain
      - fastmcp    # For MCP servers
      - pydantic   # Data validation
    avoid:
      - langchain  # Too abstracted
      - autogen    # Complexity

  development:
    machine: m1_mac
    ide: cursor, claude_code
    version_control: github

LLM Selection Matrix

LLM选型矩阵

Use Case	Primary	Fallback	Cost/1M tokens
Complex reasoning	Claude Sonnet	Gemini Pro	$3-15
Bulk processing	DeepSeek V3	Qwen 2.5	$0.14-0.27
Code generation	Claude Sonnet	DeepSeek Coder	$3-15
Embeddings	Voyage	Cohere	$0.10-0.13
Vision	Claude/Gemini	Qwen VL	$3-15
Local/Private	Ollama Qwen	Ollama Llama	Free

Cost Optimization Rule: Use Chinese LLMs (DeepSeek, Qwen) for 90%+ cost savings on bulk/routine tasks. Reserve Claude/Gemini for complex reasoning.

使用场景	首选方案	备选方案	每百万Token成本
复杂推理	Claude Sonnet	Gemini Pro	$3-15
批量处理	DeepSeek V3	Qwen 2.5	$0.14-0.27
代码生成	Claude Sonnet	DeepSeek Coder	$3-15
嵌入模型	Voyage	Cohere	$0.10-0.13
视觉任务	Claude/Gemini	Qwen VL	$3-15
本地/私有部署	Ollama Qwen	Ollama Llama	免费

成本优化规则： 对于批量/常规任务，使用中文LLM（DeepSeek、Qwen）可节省90%以上成本。仅在复杂推理场景使用Claude/Gemini。

Framework Evaluation Checklist

框架评估检查清单

markdown

undefined

markdown

undefined

[Framework Name] Evaluation

[框架名称] 评估

Basic Info

基础信息

Technical Fit

技术适配性

Ecosystem

生态系统

Red Flags

风险信号

API Evaluation Template

API评估模板

yaml

api_evaluation:
  name: ""
  provider: ""
  documentation_url: ""

  access:
    auth_method: ""  # API key, OAuth, etc.
    rate_limits:
      requests_per_minute: 0
      tokens_per_minute: 0
    quotas: ""

  pricing:
    model: ""  # per request, per token, subscription
    free_tier: ""
    cost_estimate: ""  # for our use case

  developer_experience:
    sdk_quality: ""  # 1-5
    documentation: ""  # 1-5
    error_messages: ""  # 1-5
    response_time: ""  # ms

  integration:
    existing_mcps: []
    sdk_languages: []
    webhook_support: bool

  verdict: ""  # USE, MAYBE, SKIP
  notes: ""

yaml

api_evaluation:
  name: ""
  provider: ""
  documentation_url: ""

  access:
    auth_method: ""  # API key, OAuth, etc.
    rate_limits:
      requests_per_minute: 0
      tokens_per_minute: 0
    quotas: ""

  pricing:
    model: ""  # per request, per token, subscription
    free_tier: ""
    cost_estimate: ""  # for our use case

  developer_experience:
    sdk_quality: ""  # 1-5
    documentation: ""  # 1-5
    error_messages: ""  # 1-5
    response_time: ""  # ms

  integration:
    existing_mcps: []
    sdk_languages: []
    webhook_support: bool

  verdict: ""  # USE, MAYBE, SKIP
  notes: ""

Technical Research Workflow

技术调研工作流

┌─────────────────────────────────────────────┐
│ 1. DEFINE                                    │
│    What problem are we solving?              │
│    What are the requirements?                │
│    What are the constraints?                 │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 2. DISCOVER                                  │
│    Search GitHub, HuggingFace, blogs         │
│    Check Context7 for docs                   │
│    Review existing tk_projects               │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 3. EVALUATE                                  │
│    Apply checklist above                     │
│    Test minimal example                      │
│    Check M1 compatibility                    │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 4. DECIDE                                    │
│    Build vs buy vs skip                      │
│    Document decision rationale               │
│    Update AI_MODEL_SELECTION_GUIDE if LLM    │
└─────────────────────────────────────────────┘

┌─────────────────────────────────────────────┐
│ 1. 明确需求                                  │
│    待解决的问题是什么？                        │
│    需求要求有哪些？                            │
│    约束条件是什么？                            │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 2. 资源挖掘                                  │
│    搜索GitHub、HuggingFace、技术博客          │
│    查阅Context7文档                          │
│    参考现有tk_projects项目                   │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 3. 评估验证                                  │
│    应用上述检查清单                          │
│    测试最小示例                              │
│    检查M1兼容性                              │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│ 4. 决策输出                                  │
│    自研vs采购vs放弃                          │
│    记录决策依据                              │
│    若涉及LLM，更新AI_MODEL_SELECTION_GUIDE文档 │
└─────────────────────────────────────────────┘

MCP Discovery Workflow

MCP能力挖掘工作流

python

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python

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When looking for MCP capabilities:

查找MCP能力时的步骤：

Check mcp-server-cookbook first └── /Users/tmkipper/Desktop/tk_projects/mcp-server-cookbook/
Search official MCP servers └── github.com/modelcontextprotocol/servers
Search community servers └── github.com search: "mcp server" + [capability]
Check if FastMCP wrapper exists └── Can we build it quickly?
Evaluate build vs. use existing └── Time to integrate vs. time to build

---

首先查阅mcp-server-cookbook └── /Users/tmkipper/Desktop/tk_projects/mcp-server-cookbook/
搜索官方MCP服务器 └── github.com/modelcontextprotocol/servers
搜索社区贡献的服务器 └── GitHub搜索："mcp server" + [所需能力]
检查是否存在FastMCP封装 └── 能否快速构建？
评估自研vs使用现有资源 └── 集成时间vs自研时间

---

Part 3: Combined Research Outputs

第三部分：综合调研输出

Research Report Template

研究报告模板

yaml

research_report:
  title: ""
  type: ""  # market, technical, hybrid
  date: ""
  researcher: ""

  # Executive Summary
  summary:
    question: ""
    answer: ""
    confidence: ""  # high, medium, low

  # Findings
  market_findings:
    companies_analyzed: []
    competitive_landscape: ""
    market_size: ""
    trends: []

  technical_findings:
    frameworks_evaluated: []
    recommended_stack: {}
    integration_considerations: []
    cost_analysis: {}

  # Recommendations
  recommendations:
    primary: ""
    alternatives: []
    risks: []
    next_steps: []

  # Sources
  sources:
    - type: ""
      url: ""
      date_accessed: ""
      key_findings: []

yaml

research_report:
  title: ""
  type: ""  # market, technical, hybrid
  date: ""
  researcher: ""

  # 执行摘要
  summary:
    question: ""
    answer: ""
    confidence: ""  # high, medium, low

  # 调研发现
  market_findings:
    companies_analyzed: []
    competitive_landscape: ""
    market_size: ""
    trends: []

  technical_findings:
    frameworks_evaluated: []
    recommended_stack: {}
    integration_considerations: []
    cost_analysis: {}

  # 建议
  recommendations:
    primary: ""
    alternatives: []
    risks: []
    next_steps: []

  # 信息来源
  sources:
    - type: ""
      url: ""
      date_accessed: ""
      key_findings: []

Decision Matrix Template

决策矩阵模板

Criteria	Weight	Option A	Option B	Option C
[Criterion 1]	25%	/10	/10	/10
[Criterion 2]	20%	/10	/10	/10
[Criterion 3]	20%	/10	/10	/10
[Criterion 4]	20%	/10	/10	/10
[Criterion 5]	15%	/10	/10	/10
Weighted Total	100%	/10	/10	/10

评估标准	权重	选项A	选项B	选项C
[标准1]	25%	/10	/10	/10
[标准2]	20%	/10	/10	/10
[标准3]	20%	/10	/10	/10
[标准4]	20%	/10	/10	/10
[标准5]	15%	/10	/10	/10
加权总分	100%	/10	/10	/10

Integration Notes

集成说明

Market Research

市场调研

Feeds into: dealer-scraper (enrichment), sales-agent (qualification)
Data sources: LinkedIn, Glassdoor, Indeed, G2, Capterra, Google
Pairs with: sales-outreach-skill (messaging), opportunity-evaluator-skill (deals)

输出对接： dealer-scraper（数据 enrichment）、sales-agent（线索资质判定）
数据来源： LinkedIn、Glassdoor、Indeed、G2、Capterra、谷歌
配套工具： sales-outreach-skill（消息触达）、opportunity-evaluator-skill（交易评估）

Technical Research

技术调研

References: AI_MODEL_SELECTION_GUIDE.md, runpod-deployment-skill
Projects: ai-cost-optimizer, mcp-server-cookbook
Tools: Context7 MCP for docs, HuggingFace MCP for models
Pairs with: opportunity-evaluator-skill (build vs partner decisions)

参考文档： AI_MODEL_SELECTION_GUIDE.md、runpod-deployment-skill
关联项目： ai-cost-optimizer、mcp-server-cookbook
使用工具： Context7 MCP（文档查询）、HuggingFace MCP（模型查询）
配套工具： opportunity-evaluator-skill（自研vs合作决策）

Reference Files

参考文件

Market Research

市场调研

```
reference/market.md
```
- Company profiles, tech stack discovery, ICP, competitive analysis

```
reference/market.md
```
- 企业档案、技术栈挖掘、理想客户画像（ICP）、竞品分析

Technical Research

技术调研

```
reference/technical.md
```
- Framework comparison, LLM evaluation, API patterns, MCP discovery

```
reference/technical.md
```
- 框架对比、LLM评估、API模式、MCP能力挖掘