Agent Skills: anysite Market Research

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 Output:

• Market evolution timeline
• Technology adoption curves
• Sentiment analysis
• Opportunity identification
• Risk assessment

Use export_data(cache_key, "csv") for trend data tables.

MCP Tools Reference (v2)

Data Fetching

• execute(source, category, endpoint, params) -- Universal data fetcher; always returns cache_key

Pagination

• get_page(cache_key, offset, limit) -- Load additional pages from a previous execute()

Analysis

• query_cache(cache_key, conditions, sort_by, aggregate, group_by) -- Filter, sort, and aggregate cached data

Export

• export_data(cache_key, format) -- Export to CSV, JSON, or JSONL; returns download URL

Y Combinator Research

• execute("yc", "search", "search", {"query": ...}) -- Find startups by industry, batch, filters
• execute("yc", "company", "get", {"slug": ...}) -- Get detailed company profile

SEC Research

• execute("sec", "search", "search", {"query": ...}) -- Find public companies and filings
• execute("sec", "document", "get", {"url": ...}) -- Get full document content

Social Intelligence

• execute("reddit", "search", "search", {"query": ...}) -- Community insights and sentiment
• execute("twitter", "search", "search_users", {"query": ...}) -- Real-time market pulse
• execute("linkedin", "post", "search_posts", {"keywords": ...}) -- Professional trends

Company Intelligence

• execute("linkedin", "search", "search_companies", {"keywords": ...}) -- Find companies
• execute("linkedin", "company", "get", {"company": ...}) -- Company details
• execute("webparser", "parse", "parse", {"url": ...}) -- Extract website data

Market Discovery

• Use discover(source, category) to explore available endpoints for any source
• execute("webparser", "parse", "parse", {"url": ...}) -- Scrape any URL for market data

Note: Crunchbase endpoints are disabled in v2. Use LinkedIn company search and Y Combinator data as alternatives for company research.

Market Analysis Frameworks

TAM/SAM/SOM Analysis:

Total Addressable Market (TAM):
- Count YC companies in category x avg market size
- SEC filing market size mentions
- Industry reports via execute("webparser", "parse", "parse", {"url": report_url})

Serviceable Addressable Market (SAM):
- Filter by geography, segment using query_cache()
- LinkedIn company search by ICP
- YC companies by batch/stage

Serviceable Obtainable Market (SOM):
- Realistic capture based on competition
- Competitive analysis via LinkedIn/social
- Market share indicators

Porter's Five Forces:

Using anysite v2 data:

1. Competitive Rivalry:
   - YC startups in space
   - LinkedIn company counts
   - Social mention volume

2. Threat of New Entrants:
   - Recent YC batches
   - Funding announcements
   - Talent movement (LinkedIn)

3. Supplier Power:
   - Technology dependencies
   - Integration partners

4. Buyer Power:
   - Customer reviews (Reddit)
   - Pricing transparency
   - Switching costs mentioned

5. Threat of Substitutes:
   - Alternative solutions
   - Adjacent markets

Output Formats

Chat Summary:

• Key market insights
• Competitive landscape summary
• Opportunity identification
• Strategic recommendations

CSV Export (via export_data(cache_key, "csv")):

• Company list with metrics
• Market segmentation data
• Trend indicators

JSON Export (via export_data(cache_key, "json")):

• Complete research data
• Time-series analysis
• Cross-platform correlations

Reference Documentation

• RESEARCH_METHODS.md - Market research methodologies, analysis frameworks, and data synthesis techniques

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