vc-curated-match

Match startups to venture capital funds using curated tag data.

581|60|Updated Apr 13, 2026

Search Tags:#venture capital #startup funding #startup analysis #VC matching #investment matching

Ready to run

npx skills add https://github.com/Varnan-Tech/opendirectory --skill vc-curated-match

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vc-curated-match
Source: https://github.com/Varnan-Tech/opendirectory/tree/main/skills/vc-curated-match
Command: npx skills add https://github.com/Varnan-Tech/opendirectory --skill vc-curated-match

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the problem of finding relevant venture capital funds for startups based on their product description and URL.

Core Features & Use Cases

Curated Dataset: Utilizes a static, curated dataset of top global venture capital firms.
Tag Matching: Matches project inputs to the dataset based on industry tags, stage, and geography.
Confidence Tiers: Outputs confidence tiers (High, Medium, Low) to indicate the precision of the fit.
Usage Scenarios: Useful for founders seeking funding, investors looking for investment opportunities, or anyone involved in startup ecosystem activities.

Quick Start

Run the script with the product description and URL, e.g.:

python scripts/run.py --description "AI-powered B2B SaaS platform" --url "https://example.com"

Frequently Asked Questions about vc-curated-match

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find relevant venture capital funds for my startup?▼

Match startups with venture capital funds by providing a product description and URL. The tool uses a curated dataset to align your startup's industry, stage, and geography with top global VC firms, outputting confidence tiers for the fit.

Can I use a Python script to match startups with VC funding?▼

Yes, you can run the Python script with your startup's product description and URL. It processes these inputs against a static dataset of venture capital firms to identify investment matching opportunities.

How does VC matching work for early-stage startup funding?▼

VC matching works by applying tag matching to a curated dataset. It compares your product details against venture capital funds using industry tags, stage, and geography alignment to determine investment relevance for early-stage startups.

Do I need Python to run this startup analysis tool?▼

Yes, Python is required to run the script and execute the startup analysis. You also need the static dataset of venture capital fund information included in the assets to perform the investment matching.

What is the best way to evaluate startup funding fit with a curated VC dataset?▼

Use a curated VC dataset with tag matching to evaluate startup funding fit. This approach outputs confidence tiers—High, Medium, or Low—indicating the precision of alignment between your startup and global venture capital firms.

What are the limitations of static datasets for VC matching?▼

A limitation of static datasets for VC matching is that fund information may become outdated. The tool relies on a static dataset of venture capital firms, meaning recent market changes or newly raised funds might not be reflected in the investment matching results.