> ## Documentation Index > Fetch the complete documentation index at: https://www.opendirectory.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # vc-finder > Give the skill a product URL or description. It detects the industry and funding stage, identifies 5 comparable funded companies, searches who backed those c... **Category:** Research · **Version:** `0.0.1` · **Author:** `opendirectory` · **Compatibility:** All targets (claude, codex, opencode, gemini, anti-gravity, openclaw, hermes) Give the skill a product URL or description. It detects the industry and funding stage, identifies 5 comparable funded companies, searches who backed those companies (Track A), finds VCs who publish investment theses about this space (Track B), and returns a ranked sourced investor list with deep-dives and outreach hooks. ## Install ```bash theme={null} npx "@opendirectory.dev/skills" install vc-finder --target claude ``` ```bash theme={null} npx skills add Varnan-Tech/opendirectory --skill vc-finder ``` ```bash theme={null} /plugin marketplace add Varnan-Tech/opendirectory /plugin install opendirectory-gtm-skills@opendirectory-marketplace ``` ## What It Does * Fetches the product URL via Firecrawl (handles JS-rendered SPAs) or Tavily extract as fallback * Detects funding stage from CTA signals on the page (waitlist, free trial, pricing, sales CTAs) * Maps a 3-level industry taxonomy (L1 > L2 > L3) from the product page * **Curated pre-match (Step 5b):** Scores product against a verified dataset of 25 VC funds (sourced from fund websites) -- instant zero-hallucination matches with no Tavily credits consumed * **Discovers comparable companies:** Curated portfolio companies from matched funds + Tavily live search for L3-niche specifics * Track A: 5 Tavily searches to find who invested in each comparable company * Track B: 3 Tavily searches to find VCs who publish investment theses about this specific niche * Synthesizes and ranks all found VCs -- curated matches labeled "verified", Tavily matches labeled by track * Produces top 5 deep-dives with fund overview, portfolio evidence, how-to-approach, and outreach hook * Generates 3 product-specific outreach hooks (not generic advice) * Saves output to `docs/vc-intel/[product]-[date].md` **Zero-hallucination guarantee:** Every VC name, fund detail, check size, portfolio company, and thesis source in the output must trace to either (a) the curated `data/vc_funds.json` dataset (sourced from fund websites) or (b) a specific Tavily search result. The AI does not draw from training knowledge for any factual claim. ## Requirements | Requirement | Purpose | How to Set Up | | - | - | - | | Tavily API key | VC investment research (Track A and Track B) | app.tavily.com, free tier: 1000 credits/month | | Firecrawl API key | Fetching JS-rendered product pages (optional) | firecrawl.dev, free tier: 500 credits/month | Tavily is required. Firecrawl is recommended -- without it, Tavily extract is used as fallback (may miss JS-rendered content). ## Setup ```bash theme={null} cp .env.example .env # Add TAVILY_API_KEY (required) # Add FIRECRAWL_API_KEY (recommended) ``` ## How to Use ``` "Find VCs for my startup: https://example.com" "Who invests in developer tools at seed stage?" "Build me a VC target list for https://example.com" "Which funds should I pitch? https://example.com" "Find investors for my product: [paste description]" "Who backed companies like mine? https://example.com" ``` Or paste a product description directly if the URL is behind a login or returns no readable content. ## Why Two Tracks **Track A (portfolio mapping):** VCs who already wrote a check in your space. These investors have proven they understand the category, the risks, and the buyer. They need less convincing than a generalist fund. **Track B (thesis matching):** VCs who are actively publishing about your space. An investor who wrote a 2,000-word blog post about why they want to invest in CI/CD tooling is actively looking for deals. Your cold email lands in a much warmer inbox. Generic "VCs in B2B SaaS" lists skip both signals. This skill produces only VCs with named evidence for each entry. ## Output Each run produces: 1. **Product analysis**: detected industry taxonomy, stage, ICP, comparable companies used 2. **Track A table**: VCs who backed comparable companies (with evidence) 3. **Track B table**: VCs with published theses about this space (with source) 4. **Top 5 deep-dives**: fund overview, why it fits, portfolio in space, how to approach, outreach hook 5. **3 outreach hooks**: product-specific openers for cold outreach ## Cost per Run * Firecrawl: \~\$0.001 per fetch * Tavily: 10 searches at \~$0.01 each = ~$0.10 (2 comparable discovery + 5 Track A + 3 Track B) * Curated pre-match (Step 5b): \$0.00 -- local scoring against `data/vc_funds.json`, no API calls * Total: \~\$0.10 per run ## Project Structure ``` vc-finder/ ├── SKILL.md ├── README.md ├── .env.example ├── data/ │ └── vc_funds.json (25 verified funds, sourced from fund websites) ├── scripts/ │ └── match_funds.py (standalone scoring script for testing) ├── evals/ │ └── evals.json └── references/ ├── stage-signals.md └── vc-outreach-guide.md ``` ## License MIT This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.