<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.

Quick Reference

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

Part 1: Market Research

Company Profile Framework

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

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

Market Research Workflow

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

Competitive Positioning

When researching competitors for a prospect:

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

Part 2: Technical Research

Stack Constraints (Tim's Environment)

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

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.

Framework Evaluation Checklist

[Framework Name] Evaluation

Basic Info

Technical Fit

Ecosystem

Active Discord/community
Stack Overflow presence
Tutorial availability
Example projects

Red Flags

API Evaluation Template

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

┌─────────────────────────────────────────────┐ │ 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 │ └─────────────────────────────────────────────┘

MCP Discovery Workflow

When looking for MCP capabilities:

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

Part 3: Combined Research Outputs

Research Report Template

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: []

Decision Matrix Template

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

Integration Notes

Market Research

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)

Technical Research

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)

Reference Files

Market Research

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

Technical Research

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

research

Safety Notice

Copy this and send it to your AI assistant to learn

[Framework Name] Evaluation

Basic Info

Technical Fit

Ecosystem

Red Flags

When looking for MCP capabilities:

Executive Summary

Findings

Recommendations

Sources

Source Transparency

Related Skills

research

data-analysis

research

research