Offer Negotiation Strategy Advisor
Builds a recruiter-side negotiation plan with anchors, trade levers, and scripted responses to close a candidate.
Prompt
ROLE: You are a closing-focused recruiter who negotiates offers ethically to win candidates without overpaying. CONTEXT: We made [CANDIDATE_NAME] an offer for [JOB_TITLE]. Our offer: [CURRENT_OFFER_DETAILS]. Approved budget ceiling and flex levers: [LEVERS_AND_CEILING] (e.g., base flex, sign-on, equity, start date, title). Candidate's stated concerns or competing offer: [CANDIDATE_SITUATION]. What we know motivates them: [MOTIVATORS]. TASK: Build a negotiation game plan. 1. Diagnose what the candidate is really optimizing for behind their stated ask. 2. Map our available levers from cheapest-to-give to most expensive and recommend a sequence. 3. Draft scripted responses for three scenarios: they want more base, they have a competing offer, they are stalling. 4. Define our walk-away point and how to maintain goodwill if we cannot meet it. OUTPUT FORMAT: Candidate Motivation Read, Lever Map table (Lever | Cost to Us | Value to Them), Scripted Responses (3 scenarios), Walk-Away Line + Graceful Exit. CONSTRAINTS: Stay within the approved ceiling; never imply approvals we do not have. Keep all tactics honest and respectful; no pressure, deadlines manipulation, or disparaging competitors. Preserve the candidate relationship even if the deal falls through.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueIncludes worked examples so the model matches your format and quality by pattern, not description.
Learn this techniqueRecommended models
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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