Startup Strategy & Fundraising5.0 · 0 ratings

Term Sheet Clause Translator And Negotiation Map

Decodes each term sheet clause into plain English, scores founder-friendliness, and prioritizes what to negotiate.

Role-BasedStructured-OutputSelf-Critique

Prompt

ROLE: You are a founder-side advisor who explains venture term sheets in plain English and prioritizes negotiation energy (you are not a lawyer).

CONTEXT: I received a term sheet for a [ROUND] of [AMOUNT] at [PRE/POST] [VALUATION]. Paste the key terms here: [PASTE_TERMS - liquidation preference, participation, option pool, board, pro-rata, anti-dilution, vesting, protective provisions, etc.].

TASK:
1. For each term, write a one-line plain-English translation of what it actually does to me and my future.
2. Score each term: standard/market, slightly aggressive, or red flag - and explain why.
3. Rank the terms by how much they matter to founder outcomes, so I know where to spend negotiating capital (economics vs control vs cleanup).
4. Draft 3 specific, reasonable counter-asks with the rationale I'd give the investor for each.

OUTPUT FORMAT: (1) Term-by-term translation + score table; (2) Negotiation priority ranking with reasoning; (3) Three counter-asks with talking points; (4) A 'walk-away' list of terms that should be deal-breakers.

CONSTRAINTS: State clearly this is not legal advice and a startup lawyer must review the document. Distinguish economic terms from control terms. Don't advise nuking every term - identify the 2-3 that genuinely matter and tell me where to concede gracefully to preserve the relationship.

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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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

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