Skills extract-resume-data
Extract candidate name, contact details, work history, and skills from resumes.
install
source · Clone the upstream repo
git clone https://github.com/iterationlayer/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/iterationlayer/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/extract-resume-data" ~/.claude/skills/iterationlayer-skills-extract-resume-data && rm -rf "$T"
manifest:
skills/extract-resume-data/SKILL.mdsource content
Extract Resume Data
Recruiting teams and HR platforms use this recipe to automate resume screening. Upload a resume in PDF or DOCX format and receive structured JSON with candidate name, email, work history, and skills — ready for your ATS or candidate pipeline.
APIs Used
Document Extraction (1 credit per page)
Prerequisites
You need an Iteration Layer API key. Get one at platform.iterationlayer.com — free trial credits included, no credit card required.
For full integration guidance (SDKs, auth, MCP, error handling), see the Iteration Layer Integration Guide.
Implementation
curl -X POST https://api.iterationlayer.com/document-extraction/v1/extract \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "files": [ { "type": "url", "name": "resume.pdf", "url": "https://example.com/resumes/resume.pdf" } ], "schema": { "fields": [ { "name": "name", "type": "TEXT", "description": "Full name of the candidate" }, { "name": "email", "type": "EMAIL", "description": "Candidate email address" }, { "name": "experience", "type": "ARRAY", "description": "Work experience entries", "fields": [ { "name": "company", "type": "TEXT", "description": "Employer or company name" }, { "name": "role", "type": "TEXT", "description": "Job title or role" }, { "name": "start_date", "type": "DATE", "description": "Start date of employment" }, { "name": "end_date", "type": "DATE", "description": "End date of employment" } ] }, { "name": "skills", "type": "ARRAY", "description": "List of candidate skills", "fields": [ { "name": "skill", "type": "TEXT", "description": "Skill name" } ] } ] } }'
import { IterationLayer } from "iterationlayer"; const client = new IterationLayer({ apiKey: "YOUR_API_KEY" }); const result = await client.extract({ files: [ { type: "url", name: "resume.pdf", url: "https://example.com/resumes/resume.pdf", }, ], schema: { fields: [ { name: "name", type: "TEXT", description: "Full name of the candidate", }, { name: "email", type: "EMAIL", description: "Candidate email address", }, { name: "experience", type: "ARRAY", description: "Work experience entries", fields: [ { name: "company", type: "TEXT", description: "Employer or company name", }, { name: "role", type: "TEXT", description: "Job title or role", }, { name: "start_date", type: "DATE", description: "Start date of employment", }, { name: "end_date", type: "DATE", description: "End date of employment", }, ], }, { name: "skills", type: "ARRAY", description: "List of candidate skills", fields: [ { name: "skill", type: "TEXT", description: "Skill name", }, ], }, ], }, });
from iterationlayer import IterationLayer client = IterationLayer(api_key="YOUR_API_KEY") result = client.extract( files=[ { "type": "url", "name": "resume.pdf", "url": "https://example.com/resumes/resume.pdf", } ], schema={ "fields": [ { "name": "name", "type": "TEXT", "description": "Full name of the candidate", }, { "name": "email", "type": "EMAIL", "description": "Candidate email address", }, { "name": "experience", "type": "ARRAY", "description": "Work experience entries", "fields": [ { "name": "company", "type": "TEXT", "description": "Employer or company name", }, { "name": "role", "type": "TEXT", "description": "Job title or role", }, { "name": "start_date", "type": "DATE", "description": "Start date of employment", }, { "name": "end_date", "type": "DATE", "description": "End date of employment", }, ], }, { "name": "skills", "type": "ARRAY", "description": "List of candidate skills", "fields": [ { "name": "skill", "type": "TEXT", "description": "Skill name", }, ], }, ] }, )
package main import il "github.com/iterationlayer/sdk-go" func main() { client := il.NewClient("YOUR_API_KEY") result, err := client.Extract(il.ExtractRequest{ Files: []il.FileInput{ il.NewFileFromURL( "resume.pdf", "https://example.com/resumes/resume.pdf", ), }, Schema: il.ExtractionSchema{ "name": il.NewTextFieldConfig( "name", "Full name of the candidate", ), "email": il.NewEmailFieldConfig( "email", "Candidate email address", ), "experience": il.NewArrayFieldConfig( "experience", "Work experience entries", []il.FieldConfig{ il.NewTextFieldConfig( "company", "Employer or company name", ), il.NewTextFieldConfig( "role", "Job title or role", ), il.NewDateFieldConfig( "start_date", "Start date of employment", ), il.NewDateFieldConfig( "end_date", "End date of employment", ), }, ), "skills": il.NewArrayFieldConfig( "skills", "List of candidate skills", []il.FieldConfig{ il.NewTextFieldConfig( "skill", "Skill name", ), }, ), }, }) if err != nil { panic(err) } }
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Extract resume data from the file at [file URL]. Use the extract_document tool with these fields: - name (TEXT): Full name of the candidate - email (EMAIL): Candidate email address - experience (ARRAY): Each with company (TEXT), role (TEXT), start_date (DATE), end_date (DATE) - skills (ARRAY): Each with skill (TEXT)
Response
{ "success": true, "data": { "name": { "value": "Sarah Chen", "confidence": 0.99, "citations": ["Sarah Chen"] }, "email": { "value": "sarah.chen@email.com", "confidence": 0.98, "citations": ["sarah.chen@email.com"] }, "experience": { "value": [ { "company": { "value": "Stripe", "confidence": 0.97, "citations": ["Stripe, Inc."] }, "role": { "value": "Senior Software Engineer", "confidence": 0.98, "citations": ["Senior Software Engineer"] }, "start_date": { "value": "2022-03-01", "confidence": 0.94, "citations": ["March 2022"] }, "end_date": { "value": "2025-11-01", "confidence": 0.93, "citations": ["November 2025"] } }, { "company": { "value": "Shopify", "confidence": 0.97, "citations": ["Shopify"] }, "role": { "value": "Software Engineer", "confidence": 0.96, "citations": ["Software Engineer"] }, "start_date": { "value": "2019-06-01", "confidence": 0.93, "citations": ["June 2019"] }, "end_date": { "value": "2022-02-01", "confidence": 0.92, "citations": ["February 2022"] } } ], "confidence": 0.95, "citations": [] }, "skills": { "value": [ { "skill": { "value": "TypeScript", "confidence": 0.97, "citations": ["TypeScript"] } }, { "skill": { "value": "React", "confidence": 0.97, "citations": ["React"] } } ], "confidence": 0.96, "citations": [] } } }