anysite-market-research

What this skill does

# anysite Market Research

Comprehensive market research using Y Combinator, SEC, social media, and web data through anysite MCP. Analyze tech markets, research startups, and study competitive landscapes.

## Overview

- **Research startup ecosystems** via Y Combinator data
- **Analyze public companies** through SEC filings
- **Gather market intelligence** from social platforms
- **Study industry trends** across communities
- **Identify market opportunities** through data analysis

**Coverage**: 70% - Excellent for tech/startup markets; pivoted from local business to tech focus

## Supported Platforms

- ✅ **Y Combinator**: Startup research, batch analysis, founder discovery, funding data
- ✅ **SEC**: Public company filings, financial data, disclosures
- ✅ **Reddit**: Market sentiment, community insights, product discussions
- ✅ **LinkedIn**: Industry trends, company intelligence, professional discussions
- ✅ **Twitter/X**: Market pulse, news, influencer opinions
- ✅ **Web Scraping**: Company websites, industry reports, market data

## v2 MCP Tool Interface

All data fetching uses the universal `execute()` meta-tool. Always call `discover(source, category)` first if you need to verify endpoint names or parameters.

**Core workflow**:
1. `execute(source, category, endpoint, params)` -- fetch data (returns first page + `cache_key`)
2. `get_page(cache_key, offset, limit)` -- paginate through remaining results
3. `query_cache(cache_key, conditions, sort_by, aggregate, group_by)` -- filter/sort/aggregate cached data without new API calls
4. `export_data(cache_key, format)` -- export to CSV, JSON, or JSONL for deliverables

**Error handling**: check response for `llm_hint` field -- it contains actionable guidance when calls fail or return partial data.

## Quick Start

**Step 1: Define Research Scope**

Choose focus:
- Startup ecosystem: `execute("yc", "search", "search", {"query": ...})`
- Public companies: `execute("sec", "search", "search", {"query": ...})`
- Industry sentiment: `execute("reddit", "search", "search", {"query": ...})`, `execute("twitter", "search", "search_users", {"query": ...})`
- Company intelligence: `execute("linkedin", "search", "search_companies", {...})`

**Step 2: Gather Data**

Execute searches:
```
# Startup research
execute("yc", "search", "search", {"query": "fintech", "batch": "W24,S23"})

# Public company research
execute("sec", "search", "search", {"query": "tech company"})

# Market sentiment
execute("reddit", "search", "search", {"query": "fintech trends"})
→ use get_page(cache_key, offset, limit) to collect up to 100 results
```

**Step 3: Analyze Results**

Use `query_cache()` to slice data without re-fetching:
```
# Count startups by category
query_cache(cache_key, aggregate={"field": "category", "function": "count"})

# Filter high-engagement posts
query_cache(cache_key, conditions=[{"field": "score", "operator": ">", "value": 50}], sort_by={"field": "score", "order": "desc"})
```

Extract insights:
- Market size indicators
- Competitive landscape
- Technology trends
- Consumer sentiment
- Funding patterns

**Step 4: Synthesize Findings**

Use `export_data(cache_key, "csv")` or `export_data(cache_key, "json")` to deliver:
- Market opportunity assessment
- Competitive analysis
- Trend identification
- Strategic recommendations

## Common Workflows

### Workflow 1: Startup Ecosystem Analysis

**Scenario**: Analyze fintech startup landscape

**Steps**:

1. **Find Startups**
```
execute("yc", "search", "search", {
  "query": "fintech",
  "batch": "W24,S23,W23,S22"
})
→ use get_page(cache_key, offset, limit) to paginate through all results
```

2. **Categorize by Focus**
```
For each startup:
  execute("yc", "company", "get", {"slug": company_slug})

Group by:
- Payments
- Lending
- Investment/Trading
- Banking
- Insurance
- B2B fintech tools

Or use query_cache to group:
  query_cache(cache_key, group_by="category")
```

3. **Analyze Patterns**
```
Identify:
- Hot subcategories (most startups)
- Team size distribution
- Geographic concentration
- Common tech stacks (from job postings)

Use query_cache for aggregation:
  query_cache(cache_key, aggregate={"field": "team_size", "function": "avg"})
```

4. **Research Traction**
```
For promising startups:
  execute("linkedin", "search", "search_companies", {"keywords": startup_name})
  → Check employee growth

  execute("twitter", "search", "search_users", {"query": startup_name})
  → Check social presence and buzz

  execute("webparser", "parse", "parse", {"url": startup_website})
  → Check positioning and features
```

5. **Identify White Spaces**
```
Compare:
- Overcrowded categories
- Underserved segments
- Emerging opportunities
- Geographic gaps
```

**Expected Output**:
- 50-100 startup landscape map
- Category distribution
- Funding trends
- Market gaps identified
- Competitive intensity by segment

Use `export_data(cache_key, "csv")` to deliver the startup list as a spreadsheet.

### Workflow 2: Public Company Competitive Analysis

**Scenario**: Research public competitors in cloud infrastructure

**Steps**:

1. **Find Companies**
```
execute("sec", "search", "search", {
  "query": "cloud"
})
→ use get_page(cache_key, offset, limit) to collect up to 50 results
```

2. **Get Financial Data**
```
For each company:
  execute("sec", "document", "get", {"url": document_url})

Extract:
- Revenue and growth
- Operating margins
- R&D spending
- Geographic breakdown
- Risk factors mentioned
```

3. **Analyze Strategy**
```
From 10-K filings:
- Business model
- Target markets
- Competitive advantages
- Growth initiatives
- Challenges and risks
```

4. **Track Changes**
```
Compare year-over-year:
- Revenue growth trends
- Market focus shifts
- New initiatives
- Risk factor changes
```

5. **Supplement with Social Intel**
```
execute("linkedin", "search", "search_companies", {"keywords": company_name})
→ Employee count, hiring patterns

execute("linkedin", "company", "get", {"company": company_urn})
→ Company details and strategic messaging

execute("reddit", "search", "search", {"query": company_name})
→ Customer sentiment

Use query_cache to filter sentiment:
  query_cache(cache_key, conditions=[{"field": "text", "operator": "contains", "value": "review"}])
```

**Expected Output**:
- Competitive landscape map
- Financial benchmarks
- Strategic positioning
- Growth trajectories
- Market opportunities

Use `export_data(cache_key, "json")` for structured competitive data.

### Workflow 3: Industry Trend Analysis

**Scenario**: Understand AI/ML market evolution

**Steps**:

1. **YC Startup Trends**
```
execute("yc", "search", "search", {
  "query": "AI OR machine learning OR artificial intelligence"
})
→ use get_page(cache_key, offset, limit) to collect up to 200 results

Group by batch to see:
- Trend over time
- Focus area shifts
- Team size changes

query_cache(cache_key, group_by="batch", aggregate={"field": "id", "function": "count"})
```

2. **Public Market Signals**
```
execute("sec", "search", "search", {
  "query": "artificial intelligence"
})
→ use get_page(cache_key, offset, limit) to collect up to 50 results

Check 10-K mentions of:
- "AI" or "machine learning" frequency
- AI-related investments
- AI revenue segments
```

3. **Community Sentiment**
```
execute("reddit", "search", "search", {
  "query": "AI trends 2026"
})
→ use get_page(cache_key, offset, limit) to collect up to 100 results

Analyze for:
- Excitement vs. concern
- Adoption barriers
- Use case validation
- Technology maturity

query_cache(cache_key, sort_by={"field": "score", "order": "desc"})
```

4. **Professional Discussion**
```
execute("linkedin", "post", "search_posts", {
  "keywords": "artificial intelligence"
})

Check:
- Industry adoption
- Job market signals
- Skill requirements
- Thought leader opinions
```

5. **Web Intelligence**
```
For key AI companies:
  execute("webparser", "parse", "parse", {"url": website + "/blog"})
  → Technology updates, product launches
```

**Expected Outp