Vc Portfolio Research logo

Vc Portfolio Research

Organization
swan-gtm
vc-portfolio-research

Use this skill when preparing for a meeting with a VC, PE, or accelerator partner and you need to know how their portfolio companies use your product — paste the firm's portfolio URL and get a demo-safe, partner-facing analysis (product penetration, plan mix, usage, AI adoption, open pipeline) plus a separate internal sheet with the full account-level financial detail. Built for partner and emerging-customer teams; turns hours of manual portco research into one structured run.

Overview

Publisherswan-gtm
Repositorygtm-skills
Skill namevc-portfolio-research
Stars
216
Forks
103
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by swan-gtm on GitHub. Read the source before you install it.

Installation

Install the Vc Portfolio Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/swan-gtm/gtm-skills/tree/main/skills/brad-smith/vc-portfolio-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/swan-gtm/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/brad-smith/vc-portfolio-research .claude/skills/vc-portfolio-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vc Portfolio Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vc Portfolio Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vc Portfolio Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

VC portfolio research (demo-safe)

Analyzes a venture capital or private equity firm's portfolio companies to surface how those companies use your product — product penetration, plan mix, usage, AI adoption, and open CRM pipeline.

The core judgment is the demo-safe split: the partner-facing document excludes all financial figures (no ARR, revenue, or deal amounts). A separate internal spreadsheet keeps full account-level detail — including ARR — for your team only.

Typical trigger: paste a firm portfolio URL and ask for a usage / portco analysis, or a "demo-safe portfolio summary."

One-liner: paste a VC/PE portfolio URL → the skill runs → you get a partner-safe doc of product usage, AI adoption, and pipeline across the portfolio, plus an internal sheet with the ARR detail your team needs.

How it runs

  1. Extract the portfolio — fetch and parse the firm's portfolio page (with fallbacks for JS-rendered sites; a pasted company list works when the site doesn't scrape cleanly).
  2. Match accounts by email domain — resolve each portfolio company to an account in the data warehouse: paying status, plan type, tasks used (past 30 days), AI feature milestones, and ARR.
  3. Pull open pipeline — open CRM opportunities by company domain (deal name, stage, close date, owner).
  4. Produce the two-artifact output (below), then update a portfolio registry entry (firm, date, doc/sheet links, portco → domain → account IDs) so downstream skills — like pipeline-overlap analysis — can reuse the portfolio mapping without re-scraping.

Outputs

Three artifacts in a shared drive, under Partners > {Firm Name}:

  • Folder — {Firm Name} — container for the doc + sheet.
  • Partner-facing doc — {Firm Name} — Portfolio Analysis (demo-safe, no financials): executive summary of the portfolio footprint; paying-vs-free penetration and plan-tier view; usage and AI-adoption highlights (feature-level, not dollars); open opportunities with no amounts; additional insights (upsell angles, growth signals, greenfield list); and an appendix on match quality and ambiguous domains. Default sharing: internal domain only — an external link is created only with explicit confirmation.
  • Internal sheet — {Firm Name} — Account Data (never shared externally): account IDs, domains, ARR, plan, paying status, tasks (30d), plus a summary tab with the portfolio financial snapshot and plan breakdown.

When to use it

  • Preparing for a partner meeting with a VC/PE firm
  • Building a demo-safe portfolio readout to share externally (after confirmation)
  • Mapping product footprint and AI adoption across a fund's companies
  • Feeding the portfolio into later pipeline-overlap work

When NOT to use it

  • Single-account ARR lookups
  • General product questions
  • Partnership strategy without a portfolio data pull

Prerequisites

  • A data-warehouse connection (Brad's implementation: Databricks via MCP, with the CRM's deal/company tables synced into the warehouse)
  • A docs/sheets/drive connection for the output artifacts (his implementation: Google Workspace via the Zapier MCP)
  • A portfolio URL — or a pasted company list if the site doesn't scrape cleanly

What good looks like

The partner doc contains zero dollar figures anywhere — penetration, plans, usage, and pipeline stages tell the story without ARR. External sharing never happens by default; it is gated on an explicit confirmation. The internal sheet stays internal, always. Ambiguous domain matches are disclosed in the appendix rather than silently counted. And the portfolio registry is updated on every run, so the next skill that needs this firm's portco mapping starts from the registry instead of re-scraping.


This page documents Brad's production workflow at Zapier, where it runs as a Cursor Agent Skill against their warehouse. The pattern — a demo-safe external doc, an internal-only financial sheet, and a reusable portfolio registry — replicates on any warehouse + CRM + docs stack.

Frequently asked questions

What does the Vc Portfolio Research AI skill do?

Use this skill when preparing for a meeting with a VC, PE, or accelerator partner and you need to know how their portfolio companies use your product — paste the firm's portfolio URL and get a demo-safe, partner-facing analysis (product penetration, plan mix, usage, AI adoption, open pipeline) plus a separate internal sheet with the full account-level financial detail. Built for partner and emerging-customer teams; turns hours of manual portco research into one structured run.

Why use Vc Portfolio Research on TypingMind?

Because you install it once and use it with any model. Vc Portfolio Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vc Portfolio Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/swan-gtm/gtm-skills/tree/main/skills/brad-smith/vc-portfolio-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vc Portfolio Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vc Portfolio Research?

As many as you like. As long as a model supports skills, you can use Vc Portfolio Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vc Portfolio Research AI skill free?

Yes. It is published on GitHub by swan-gtm under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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