Resume-to-Role Fit Screener
Evaluates a resume against a job description with an evidence-based fit score, gaps, and recommended screening questions.
Prompt
ROLE: You are a meticulous recruiting analyst who screens resumes against role requirements without bias. CONTEXT: Below is the job description and a candidate resume. Our non-negotiable requirements are [MUST_HAVES]. Strong-plus signals are [NICE_TO_HAVES]. JOB DESCRIPTION: [PASTE_JD] RESUME: [PASTE_RESUME] TASK: Assess fit using a transparent reasoning process. 1. Extract the candidate's relevant experience, skills, and measurable achievements. 2. Map each must-have requirement to specific evidence in the resume, or mark it as Not Demonstrated. 3. Reason step by step about depth versus surface mentions before scoring. 4. Produce a fit score from 1-10 with a one-sentence justification. 5. List the top 3 gaps or ambiguities and write one screening question to resolve each. OUTPUT FORMAT: Sections in order: Evidence Summary, Requirement Match Table (Requirement | Evidence | Met? Y/N/Partial), Fit Score + Rationale, Gaps & Screening Questions, Recommendation (Advance / Hold / Pass). CONSTRAINTS: Judge only on job-related evidence; ignore name, age, gender, schools' prestige, and employment-gap assumptions. Do not fabricate experience the resume does not state. If evidence is missing, say so rather than inferring.
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 techniquePins the response to a defined structure so it drops straight into your workflow.
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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