name: accelerator-research-agent description: Research accelerator portfolio companies using Firecrawl and Tavily MCPs. Generates structured CSV and markdown reports with systematic impact scoring. Optimized for token efficiency.

Accelerator Research Agent

A token-optimized Claude Desktop skill for researching accelerator portfolio companies with systematic impact analysis.

When to Use This Skill

Activate when user asks to:

"Research companies from [accelerator name]"
"Analyze [accelerator] portfolio"
"Score companies for impact" or "evaluate mission alignment"
Mentions: YC, Techstars, Fast Forward, 500 Global, a16z

Prerequisites

Required MCP Servers (both tested and validated):

Firecrawl MCP - Structured extraction
- Free tier: 500 credits/month
- Use firecrawl_extract for JSON extraction
Tavily MCP - AI-optimized search
- Free tier: 100 RPM (6,000/hour)
- Use tavily-search for company research

Core Workflow (3 Phases)

Phase 1: Portfolio Extraction

Goal: Get company list from accelerator portfolio page

Tool: firecrawl_extract (PRIMARY - 100% success rate)

Schema Pattern: See SCHEMA-TEMPLATES.md for tested schemas (YC, Fast Forward, Healthcare, Climate, Fintech)

Quick Schema (customize based on accelerator):

{
  "name": "mcp__MCP_DOCKER__firecrawl_extract",
  "arguments": {
    "urls": ["PORTFOLIO_URL"],
    "prompt": "Extract all portfolio companies including name, website, description, industry",
    "schema": {
      "type": "object",
      "properties": {
        "companies": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": {"type": "string"},
              "website": {"type": "string"},
              "description": {"type": "string"},
              "industry": {"type": "string"}
            },
            "required": ["name"]
          }
        }
      },
      "required": ["companies"]
    }
  }
}

Token Optimization:

Only require "name" field
Use string types for all fields (more flexible)
Add "maxAge": 604800000 for caching (7 days)

If Extract Fails - Use fallback:

{
  "name": "mcp__MCP_DOCKER__firecrawl_scrape",
  "arguments": {
    "url": "PORTFOLIO_URL",
    "formats": ["markdown"]
  }
}

Then manually parse the markdown.

Phase 2: Company Research

Goal: Research each company using web search

Tool: tavily-search with token-efficient parameters

CRITICAL - Token Optimization:

{
  "name": "mcp__MCP_DOCKER__tavily-search",
  "arguments": {
    "query": "[company name] mission target market",
    "max_results": 3,                    // ✅ NOT 10! Saves 70% tokens
    "search_depth": "basic",             // ✅ NOT "advanced"! Faster
    "include_raw_content": false         // ✅ Critical - saves massive tokens
  }
}

Batch Processing (IMPORTANT):

Research 3-5 companies at a time (not 10-20)
Generate incremental reports to avoid token limits

Research Query Pattern:

"[Company Name] mission target market product"

Extract from Results:

Founder names
Mission/tagline
Target market demographic
Product/service description
Key metrics (users, funding, team size)

Phase 3: Impact Scoring

Goal: Score companies using 5-tier rubric

5-Tier Impact Rubric (Customizable):

⭐⭐⭐⭐⭐ Tier 1 - Direct Impact

Primary target: Underserved populations
Core product addresses fundamental challenges
Impact central to business model

⭐⭐⭐⭐ Tier 2 - Strong Alignment

Significant focus on underserved
Clear pathway to reach target communities
Impact is key differentiator

⭐⭐⭐ Tier 3 - Moderate Alignment

Serves underserved as secondary market
Impact through indirect channels
Mixed revenue model

⭐⭐ Tier 4 - Weak Alignment

Minimal underserved focus
Impact is incidental or aspirational
Primarily serves mainstream markets

⭐ Tier 5 - Minimal Alignment

No focus on underserved
Luxury/premium positioning
Opposite of mission

Customization Examples:

Climate Tech: Direct emissions reduction → Greenwashing
Healthcare: Medicaid focus → Luxury medicine
Fintech: Unbanked → High-net-worth

Phase 4: Report Generation

CSV Format (Excel/Sheets compatible):

Company Name,Website,Description,Industry,Impact Tier,Impact Reasoning,Founder,Funding

Markdown Format:

# [Accelerator] Portfolio Research Report

## Executive Summary
- Total companies researched: X
- Impact distribution: Tier 1 (X), Tier 2 (X), etc.

## High-Impact Companies (Tier 1-2)

### Company Name
- **Website**: [URL]
- **Impact Tier**: ⭐⭐⭐⭐⭐
- **Mission**: [Brief mission]
- **Target Market**: [Demographics]
- **Why High Impact**: [Reasoning]
- **Metrics**: [Users, funding, etc.]

[Repeat for each high-impact company]

## Moderate Impact Companies (Tier 3)
[Summarized list]

## Lower Priority Companies (Tier 4-5)
[Brief list]

Token Management Best Practices

Critical for Avoiding Limits:

Batch Processing: Research 3-5 companies at a time
Tavily Parameters:
- max_results: 3 (not 10)
- search_depth: "basic" (not "advanced")
- include_raw_content: false (saves massive tokens)
Incremental Reports: Generate partial results, then continue
Schema Efficiency: Only require essential fields
Caching: Use maxAge parameter for portfolio pages

Common Scenarios

Scenario 1: YC Research

User: "Research 10 YC W25 climate tech companies"

Steps:
1. Extract YC W25 companies (firecrawl_extract + YC schema)
2. Filter to climate tech vertical (JSON filtering)
3. Research FIRST 5 companies (tavily-search, max_results=3)
4. Score and generate partial report
5. Research NEXT 5 companies (new batch)
6. Append to report

Scenario 2: Fast Forward Impact

User: "Score Fast Forward portfolio for low-income US impact"

Steps:
1. Extract Fast Forward companies (firecrawl_extract)
2. Research in batches of 3 (tavily-search)
3. Apply low-income US impact rubric
4. Generate CSV + markdown report

Scenario 3: Healthcare Medicaid

User: "Find healthcare startups serving Medicaid populations"

Steps:
1. Extract with healthcare vertical schema (see SCHEMA-TEMPLATES.md)
2. Research with query: "[company] Medicaid low-income healthcare"
3. Filter to Medicaid focus
4. Score using healthcare impact rubric

Troubleshooting

Token Limit Hit:

Reduce batch size to 3 companies
Use search_depth: "basic"
Set include_raw_content: false
Generate incremental reports

Extract Returns Empty:

Check SCHEMA-TEMPLATES.md for validated schemas
Improve prompt specificity
Try fallback to firecrawl_scrape

Search Returns Poor Results:

Refine query: "[company name] mission target market"
Reduce max_results to 3
Try alternative search: "[company name] about"

Files Reference

SCHEMA-TEMPLATES.md: Production-tested extraction schemas
README.md: Setup instructions and MCP configuration

Output Deliverables

This skill generates ONLY research outputs:

✅ CSV file with all company data
✅ Markdown report with analysis

This skill does NOT:

❌ Create Linear/project tracking issues
❌ Integrate with CRM systems
❌ Send notifications

Use separate skills for pipeline management if needed.

Version: 2.1 (Token-Optimized) | Testing: Validated on YC, Fast Forward

accelerator-research-agent

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