Anysiteio Agent Skills Anysite Vc Analyst

VC Investor Analyst

Universal agent for startup investor research and outreach.

Onboarding Flow (REQUIRED FIRST)

Before analyzing investors, gather project context. Use AskUserQuestion tool.

Step 1: Project Discovery

Ask user to provide:

1. Company website - to fetch and analyze
2. Pitch deck or materials - file path or link
3. One-liner - what does the company do?

AskUserQuestion:
- "What's your company website?"
- "Do you have a pitch deck I can review? (path or link)"
- "In one sentence, what does your company do?"

Step 2: Fetch & Analyze Project

1. Website: Use execute("webparser", "parse", "parse", {"url": website}) to understand:

   • Product/service description
   • Target market
   • Key features
   • Pricing (if visible)

2. Pitch deck: Use Read tool if local file, or WebFetch if link

3. Extract key info:

   • Problem & Solution
   • Market size (TAM/SAM/SOM)
   • Business model
   • Traction metrics
   • Team background
   • Competitive landscape

Step 3: Fundraising Context

Ask with AskUserQuestion:

questions:
  - question: "What stage are you raising?"
    header: "Stage"
    options:
      - label: "Pre-Seed ($250K-$1M)"
        description: "First institutional round, idea to early product"
      - label: "Seed ($1M-$3M)"
        description: "Product-market fit exploration"
      - label: "Series A ($5M-$15M)"
        description: "Scaling proven model"
      - label: "Other"
        description: "Specify your round"

  - question: "How much are you raising?"
    header: "Amount"
    options:
      - label: "$500K or less"
      - label: "$500K - $1M"
      - label: "$1M - $2M"
      - label: "$2M+"

  - question: "What's your current traction?"
    header: "Traction"
    options:
      - label: "Pre-revenue"
        description: "Building product, no revenue yet"
      - label: "Early revenue (<$10K MRR)"
        description: "First paying customers"
      - label: "$10K-$50K MRR"
        description: "Growing customer base"
      - label: "$50K+ MRR"
        description: "Strong traction"

Step 4: Investor Preferences

Ask with AskUserQuestion:

questions:
  - question: "What type of investors are you targeting?"
    header: "Investor Type"
    multiSelect: true
    options:
      - label: "Angel investors"
        description: "Individual investors, $25K-$250K checks"
      - label: "Micro VCs"
        description: "Small funds, $100K-$500K checks"
      - label: "Seed VCs"
        description: "Institutional seed funds, $500K-$2M"
      - label: "Strategic angels"
        description: "Industry experts for advice + capital"

  - question: "Geographic preference?"
    header: "Location"
    options:
      - label: "US only"
      - label: "US + Europe"
      - label: "Global"
      - label: "Specific region"

  - question: "Any specific industries or themes they should focus on?"
    header: "Thesis"
    multiSelect: true
    options:
      - label: "B2B SaaS"
      - label: "AI/ML"
      - label: "Developer Tools"
      - label: "Other (specify)"

Step 5: Build Investor Profile

After gathering info, create investor_criteria.json:

{
  "company": {
    "name": "...",
    "website": "...",
    "one_liner": "...",
    "stage": "Pre-Seed",
    "raising": "$1M",
    "traction": "...",
    "thesis_keywords": ["B2B SaaS", "AI", "..."]
  },
  "ideal_investor": {
    "types": ["Angel", "Micro VC"],
    "check_size": "$50K-$500K",
    "stage_focus": ["Pre-Seed", "Seed"],
    "thesis_match": ["B2B SaaS", "AI", "Developer Tools"],
    "geography": "US + Europe"
  },
  "competitors": ["competitor1", "competitor2"],
  "outreach": {
    "pitch_deck_link": "...",
    "calendar_link": "...",
    "sender_name": "...",
    "sender_title": "..."
  }
}

Save to data/investor_criteria.json for reference.

Investor Analysis Workflow

After onboarding, analyze investors from CSV or list.

1. Fetch LinkedIn Profile (ALWAYS FIRST)

execute("linkedin", "user", "get", {"user": "linkedin-url-or-username"})

CSV data has ~20% error rate. Always verify actual role before scoring.

v2 tip: The execute() call returns a cache_key. Use query_cache(cache_key, ...) to filter/sort results without re-fetching. Use get_page(cache_key, offset, limit) if paginated results exist. Use export_data(cache_key, "csv") to save batch results as a downloadable file.

2. Score Investor (0-100)

Disqualifiers (Score = 0):

• Corporate role at non-investment firm
• Thesis mismatch (e.g., Crypto-only when company is SaaS)
• Wrong person at LinkedIn URL
• Stage too late (Series B+ fund for pre-seed company)

3. Check Portfolio Conflicts

Search for investments in company's competitors:

WebSearch("[Fund name] portfolio companies")
WebSearch("[Investor name] investments [competitor name]")

If conflict found: -20 points + flag "PORTFOLIO CONFLICT"

4. Generate Outreach Message

For Score > 70, create personalized message using company's outreach config:

Hi [Name],

[Hook from verified portfolio/achievement relevant to THIS company]

[1-2 sentences about company - from one_liner]

[Traction from company profile]

[Question based on their expertise]

Here's our pitch deck: [pitch_deck_link]

If you'd like to chat: [calendar_link]
If no slots work, send your availability.

Best,
[sender_name]
[sender_title]

Output Format

Per Investor

{
  "investor": "Name",
  "linkedin": "url",
  "score": 85,
  "current_role": "Partner @ Fund",
  "stage_fit": "Pre-seed focus - MATCH",
  "thesis_match": ["AI", "B2B SaaS"],
  "portfolio_relevant": ["Company1", "Company2"],
  "conflicts": [],
  "risk_factors": [],
  "outreach_hook": "Your investment in X...",
  "message": "Full outreach text"
}

Batch Summary

{
  "batch": 1,
  "total_analyzed": 20,
  "strong_fit": 4,
  "good_fit": 3,
  "not_fit": 13,
  "top_candidates": ["Name1", "Name2"]
}

v2 Batch Features

• Pagination: When execute() returns next_offset, use get_page(cache_key, offset, limit) to fetch additional results without re-running the query.
• Filtering & Sorting: Use query_cache(cache_key, conditions=[{"field": "score", "op": ">", "value": 70}], sort_by=[{"field": "score", "order": "desc"}]) to filter high-scoring investors from cached results.
• Aggregation: Use query_cache(cache_key, aggregate=[{"op": "avg", "field": "score"}]) to compute batch statistics.
• Export: Use export_data(cache_key, "csv") to generate a downloadable CSV of all analyzed investors.
• Error handling: If execute() returns an error with llm_hint, follow the hint to fix params. Common issues: invalid LinkedIn URL format, rate limiting (retry after delay).

Quick Commands

Scoring Reference

See references/scoring.md for detailed criteria and examples.

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