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Investor Research

Community
shawnpang
investor-research

When the user wants to identify, evaluate, or prioritize potential investors for a fundraising round. Also activates when the user asks "who should I pitch?", "find me investors", "build an investor list", or mentions VC/angel targeting.

Overview

Publishershawnpang
Repositorystartup-founder-skills
Skill nameinvestor-research
Stars
368
Forks
43
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 shawnpang on GitHub. Read the source before you install it.

Installation

Install the Investor 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/shawnpang/startup-founder-skills/tree/main/skills/investor-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/shawnpang/startup-founder-skills.git /tmp/startup-founder-skills
mkdir -p .claude/skills
cp -r /tmp/startup-founder-skills/skills/investor-research .claude/skills/investor-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Investor 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 Investor 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 Investor 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.

Investor Research

When to Use

  • The founder is preparing to fundraise and needs a target investor list.
  • The founder has a list of investors and wants to qualify or prioritize them.
  • The founder asks which VCs or angels are a good fit for their stage, sector, or geography.
  • The founder wants to understand a specific fund's thesis, portfolio, or decision-making process.

Context Required

From startup-context: stage, sector/category, location, current round target (amount), business model, and any existing investor relationships or warm connections.

From the user: geographic preferences (if any), whether they want VC-only, angel-only, or both, any investors already in conversation, and any firms they want to explicitly avoid (e.g., portfolio conflicts they know about).

Workflow

  1. Read startup context — Pull stage, sector, geography, round size, and existing investors from .agents/startup-context.md.
  2. Define investor criteria — Based on context, establish the filtering parameters: stage match, sector focus, typical check size range, geographic relevance, and portfolio conflict exclusions.
  3. Build the raw list — Research investors matching the criteria. For each investor, capture: firm name, partner name, fund stage focus, sector focus, typical check size, recent fund size/vintage, portfolio companies, geographic preference, and a source URL.
  4. Check for conflicts — Flag any firm that has a portfolio company directly competing with the founder's startup. These go on a "conflicts" list, not the target list.
  5. Score and tier — Assign each investor to Tier 1 (strong fit, prioritize), Tier 2 (good fit, pursue), or Tier 3 (acceptable fit, use as backfill) using the scoring framework below.
  6. Identify warm paths — For each Tier 1 investor, suggest how the founder might get a warm intro: mutual connections, portfolio founder intros, accelerator networks, or conference overlap.
  7. Deliver the target list — Output a structured, sortable list with tiers and recommended outreach order.

Output Format

A markdown table with the following columns, grouped by tier:

## Tier 1 — High Priority

| Firm | Partner | Stage Focus | Sector Fit | Check Size | Recent Fund | Conflict? | Warm Path | Notes |
|------|---------|-------------|------------|------------|-------------|-----------|-----------|-------|

Followed by a "Conflicts" section listing excluded firms and why.

Followed by a "Research Gaps" section listing anything that could not be verified and needs the founder's input.

Frameworks & Best Practices

Investor Qualification Criteria (The 7-Point Filter)

  1. Stage fit — Does the firm invest at the founder's current stage? A Series B fund will not lead a seed round. This is the first filter and it is binary: pass or fail.
  2. Sector focus — Does the firm have a stated thesis or track record in the founder's sector? Look at their last 10 investments, not just their website copy.
  3. Check size match — Does the firm's typical check size align with what the founder needs? A $2B fund rarely writes $500K checks. A $50M fund rarely leads $20M rounds.
  4. Portfolio conflicts — Does the firm already have a company in the same space? This is the most common reason pitches are dead-on-arrival. Check every portfolio company, including quiet ones.
  5. Fund vintage — Is the firm actively deploying from a recent fund? A fund raised 4+ years ago is likely in harvest mode and not writing new checks. Prefer firms that closed a fund within the last 18 months.
  6. Geographic relevance — Some firms only invest locally. Others require board seats that demand proximity. Remote-friendly firms have expanded, but geography still matters for many funds.
  7. Partner-level interest — Is there a specific partner whose background, interests, or public writing aligns with the startup? Pitching the right partner at the right firm matters as much as pitching the right firm.

Tiering Framework

  • Tier 1: Matches on 6-7 of the criteria above. The firm has invested in adjacent companies, the partner has spoken publicly about the space, and a warm intro path exists. Pursue first.
  • Tier 2: Matches on 4-5 criteria. Good fit on stage and sector but may lack a warm path or have a slightly mismatched check size. Pursue in the second wave.
  • Tier 3: Matches on 3 criteria. Acceptable as backfill if the round needs more participants. Do not spend significant time here until Tier 1 and 2 are exhausted.

Sourcing Investor Information

  • Crunchbase / PitchBook: Fund size, recent investments, portfolio companies.
  • Firm website: Stated thesis, partner bios, blog posts that reveal focus areas.
  • Twitter/X and Substack: Many partners publish their current interests publicly. Recent posts are a better signal than old "About" pages.
  • SEC filings: Fund size from Form D filings when not publicly disclosed.
  • Portfolio founder back-channels: The single best diligence on an investor is talking to founders they have backed — both successes and companies that struggled.

Common Mistakes to Avoid

  • Spraying 200 cold emails — Fundraising is a funnel. 30 well-targeted, well-introduced conversations beat 200 cold ones.
  • Ignoring portfolio conflicts — Founders waste weeks pitching firms that will never invest because of a conflict.
  • Pitching the wrong partner — At multi-partner firms, the wrong partner will say "interesting, let me introduce you to my colleague" at best, or just pass.
  • Targeting only brand-name firms — Tier 2 and emerging funds are often faster to decide, more founder-friendly, and more willing to lead at earlier stages.
  • Not tracking your pipeline — Use a simple spreadsheet or CRM: investor name, status (researching / intro requested / meeting scheduled / pitched / passed / term sheet), and next action.

Angel Investor Considerations

  • Angels decide faster (days, not weeks) but write smaller checks ($25K-$250K typically).
  • Look for angels with operational experience in your sector — they add value beyond capital.
  • Angel syndicates (AngelList, etc.) can aggregate small checks into a meaningful allocation.
  • Be cautious about taking angel money from potential acquirers or competitors without understanding the signaling implications.

Related Skills

  • pitch-deck — tailor the deck narrative based on what specific investors care about
  • fundraising-email — write targeted outreach once the investor list is built

Examples

Example prompt: "We're raising a $2.5M seed round for a developer tools company based in SF. Help me build an investor list."

Good output snippet (one Tier 1 entry):

| Boldstart Ventures | Ed Sim | Pre-seed/Seed | Developer tools, infrastructure | $1-3M | $160M Fund IV (2023) | None | Ed is active on Twitter re: dev tools; check if any portfolio founders overlap with your network | Led seed in [similar company]; blog post on "Why developer experience is the next platform shift" |

Example prompt: "I have a list of 15 VCs I want to pitch. Can you help me prioritize?"

Good output approach: Run each firm through the 7-point filter against the founder's startup context. Re-tier the list. Flag any portfolio conflicts the founder may have missed. Identify the 5 to pitch first and suggest the outreach sequence.

Frequently asked questions

What does the Investor Research AI skill do?

When the user wants to identify, evaluate, or prioritize potential investors for a fundraising round. Also activates when the user asks "who should I pitch?", "find me investors", "build an investor list", or mentions VC/angel targeting.

Why use Investor Research on TypingMind?

Because you install it once and use it with any model. Investor 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 Investor Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shawnpang/startup-founder-skills/tree/main/skills/investor-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Investor 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 Investor Research?

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

Is the Investor Research AI skill free?

Yes. It is published on GitHub by shawnpang 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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