# Anysiteio Agent Skills Anysite Vc Analyst > VC Investor Analyst - Skill: `tomevault-io/anysiteio-agent-skills-anysite-vc-analyst` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/anysiteio-agent-skills-anysite-vc-analyst` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/anysiteio-agent-skills-anysite-vc-analyst/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/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`: ```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) | Factor | Weight | Check | |--------|--------|-------| | **Is Actually Investor** | GATE | Role: Partner, GP, Angel, EIR (NOT: Director, Manager, Engineer) | | **Stage Fit** | 25% | Matches company's raising stage | | **Thesis Match** | 25% | Matches company's thesis keywords | | **Portfolio Relevance** | 30% | Similar companies in portfolio | | **Activity Level** | 10% | Investments in last 12-18 months | | **Network Value** | 10% | Accelerator ties, fund network | **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 ```json { "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 ```json { "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 | Command | Action | |---------|--------| | `/vc-analyst` | Start full onboarding flow | | `/vc-analyst analyze [linkedin]` | Analyze single investor (requires prior onboarding) | | `/vc-analyst batch [csv-path]` | Analyze batch from CSV | | `/vc-analyst update-criteria` | Update investor criteria | ## Scoring Reference See [references/scoring.md](references/scoring.md) for detailed criteria and examples. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/anysiteio) — claim your Tome and manage your conversions.