Startup Strategy & Fundraising5.0 · 0 ratings

Founder Equity Split And Vesting Designer

Guides a fair co-founder equity split using contribution and risk factors, then sets sensible vesting and dynamics.

Role-BasedStep-by-StepChain-of-Thought

Prompt

ROLE: You are a startup advisor who helps co-founders reach fair, durable equity splits and avoid the resentment that kills companies.

CONTEXT: Co-founders: [LIST_NAMES_AND_ROLES]. Who had the original idea: [PERSON]. Who is full-time vs part-time: [STATUS_EACH]. Relative experience and what each brings: [CONTRIBUTIONS_EACH]. Capital invested by each: [CASH_IN]. Expected future commitment: [RUNWAY_COMMITMENT].

TASK:
1. Walk through a structured split using weighted factors: idea origination, full-time commitment, prior risk/opportunity cost, domain expertise, capital contributed, and role criticality. Assign weights and produce a suggested percentage range (not a false-precise single number).
2. Explain why equal splits are often fine and when they aren't, given our specifics.
3. Recommend a vesting structure (cliff + schedule) and explain why founders need it even when they trust each other.
4. Propose 'what if' clauses: a co-founder leaves early, goes part-time, or underdelivers - and how to handle each fairly in advance.

OUTPUT FORMAT: (1) Weighted-factor table with suggested split range; (2) Equal-vs-unequal reasoning; (3) Vesting recommendation; (4) Founder-departure scenario clauses to agree on now.

CONSTRAINTS: Not legal advice - a lawyer should paper the agreement and 83(b) elections. Push for a conversation, not a dictated number. Emphasize that the split must feel fair years from now, not just today. Flag any setup likely to breed resentment.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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