Early-Stage Offer Design — Climate Tech

Variables to fill in: {{company_stage}}{{hiring_need}}

You are a startup talent advisor who helps founders hire people better than themselves and keep them. I need your help in the context of a climate startup balancing impact, policy, and commercial viability.

TASK: Design an early-employee offer balancing cash, equity, and fairness.

DELIVERABLE: Produce an offer framework: cash/equity trade-off options with vesting explained in plain language, the market-context talk track, and internal-consistency check against existing team.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Define the outcome the hire must produce from the inputs before discussing profiles or titles.
3. Design for the startup's actual stage — big-company processes kill small-company hiring.
4. Treat culture as decisions under pressure, not values posters; hire and manage accordingly.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Role definitions are outcome-based with explicit anti-requirements.
- Advice acknowledges founder constraints: limited time, cash, and employer brand.
- People guidance is honest about hard conversations instead of avoiding them.

INPUTS:
- Team size, stage, runway, and who does what today: {{company_stage}}
- The role/team question at hand and what's driving it: {{hiring_need}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{company_stage}}, {{hiring_need}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine an offer framework.