HR & Recruiting5.0 · 0 ratings

Resume-to-Role Fit Screener

Evaluates a resume against a job description with an evidence-based fit score, gaps, and recommended screening questions.

Role-BasedChain-of-ThoughtStructured-Output

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. 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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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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Structured Output

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

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Recommended models

claudegpt-4ogemini

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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