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AI Resume Screener workflow blueprint

Score one resume against a job description with evidence-checked skill matching and a deterministic rule audit

See where this blueprint fits in the production workflow lifecycle
Parallel
HR
Recruiting
Resume
Screening
Use this blueprint
Pattern
Parallel
Agents
1
Functions
2
MCP servers
0

What the Resume Screener blueprint does

Suggested starting request

I screen resumes against a job description. Send the resume as a PDF, DOCX, or XLSX (URL, base64 or data: URI, or a storage "id/key") or as plain text, plus the job description as text or a URL. I return a scored match: 0-100 score, recommendation, matched/missing skills, experience fit, red flags, interview questions, and a recruiter-ready report. The reply also carries the full structured screening object and a validation log of any deterministic corrections applied.

Inputs for the Resume Screener workflow

Provide the following information when starting this workflow. Required inputs are validated before the first workflow stage runs.

  • Resume

    Required

    Resume as a document (URL, base64/data: URI, or storage 'id/key') or plain text

    Type: String

  • Job Description

    Required

    Job description as plain text, or a URL to a JD page or document

    Type: String

  • Threshold

    Optional

    Score cutoff for strong_match (default 70)

    Type: Number

  • Required Skills

    Optional

    Explicit must-have skills list

    Type: Array

  • Min Experience Years

    Optional

    Hard minimum years of experience

    Type: Number

Input template

This request shape is generated from the blueprint's input schema. Angle-bracket values are placeholders; defaults and examples appear only when the source blueprint defines them.

{
  "resume": "<required string>",
  "job_description": "<required string>",
  "threshold": "<optional number>",
  "required_skills": "<optional array>",
  "min_experience_years": "<optional number>"
}

How the Resume Screener AI workflow works

This hr & people blueprint uses a parallel workflow with 1 agent, 2 functions, and 0 MCP servers. Open it in GraphN to customize the resources, inputs, and workflow behavior for your use case.

  1. Extract Resume

    Function using Extract Doc

  2. Extract Jd

    Function using Extract Doc

  3. Screen

    Agent using Resume Screener

  4. Validate

    Function using Validate Screen

Blueprint resources and prerequisites

These are the actual agents, functions, and MCP servers declared by the blueprint. If an MCP server connects to an external system, configure the required connection and credentials in your GraphN workspace before running the workflow.

AI agent

Resume Screener

Function

Extract Doc

Function

Validate Screen

Workflow output contract

The completed workflow makes the following output available to downstream steps, applications, or API clients:

Result

Frequently asked questions

What does the Resume Screener blueprint do?
Score one resume against a job description with evidence-checked skill matching and a deterministic rule audit
What inputs does the Resume Screener workflow use?
The workflow uses Resume, Job Description, Threshold, Required Skills, Min Experience Years. Required fields and their expected formats are listed on this page.
What resources are included in the Resume Screener blueprint?
The blueprint contains 1 AI agent, 2 functions, 0 MCP servers. Its workflow pattern is Parallel.
Can I customize the Resume Screener blueprint in GraphN?
Yes. Open the blueprint in GraphN to customize its inputs, models, resources, and workflow behavior before deployment.

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