Resize, sharpen, and compress an image to fit email platform size limits in a single pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
iterationlayer/skills42026年6月9日 更新
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extract-resume-data
Extract candidate name, contact details, work history, and skills from resumes.
含まれるファイル（1）
- SKILL.md13.3 KB
SKILL.md（原文）
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Extract Resume Data
Recruiting teams and HR platforms use this recipe to use the Iteration Layer Document Extraction API as a resume parser API for PDF and DOCX resumes. Upload a resume 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 during the 7-day trial.
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.extractDocument({
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_document(
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.ExtractDocument(il.ExtractDocumentRequest{
Files: []il.FileInput{
il.FileInput{
Type: "url",
Name: "resume.pdf",
Url: "https://example.com/resumes/resume.pdf",
},
},
Schema: il.ExtractionSchema{
Fields: []any{
il.TextFieldConfig{
Name: "name",
Type: "TEXT",
Description: "Full name of the candidate",
},
il.EmailFieldConfig{
Name: "email",
Type: "EMAIL",
Description: "Candidate email address",
},
il.ArrayFieldConfig{
Name: "experience",
Type: "ARRAY",
Description: "Work experience entries",
Fields: []any{
il.TextFieldConfig{
Name: "company",
Type: "TEXT",
Description: "Employer or company name",
},
il.TextFieldConfig{
Name: "role",
Type: "TEXT",
Description: "Job title or role",
},
il.DateFieldConfig{
Name: "start_date",
Type: "DATE",
Description: "Start date of employment",
},
il.DateFieldConfig{
Name: "end_date",
Type: "DATE",
Description: "End date of employment",
},
},
},
il.ArrayFieldConfig{
Name: "skills",
Type: "ARRAY",
Description: "List of candidate skills",
Fields: []any{
il.TextFieldConfig{
Name: "skill",
Type: "TEXT",
Description: "Skill name",
},
},
},
},
},
})
if err != nil {
panic(err)
}
_ = result
}
{
"name": "Extract Resume Data",
"nodes": [
{
"parameters": {
"content": "## Extract Resume Data\n\nRecruiting 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 \u2014 ready for your ATS or candidate pipeline.\n\n**Note:** This workflow uses the Iteration Layer community node (`n8n-nodes-iterationlayer`). Install it via Settings > Community Nodes on self-hosted n8n, or add it directly on n8n Cloud with Verified Community Nodes enabled.",
"height": 280,
"width": 500,
"color": 2
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
200,
40
],
"id": "9ff8f73c-a387-4f6b-b9c0-3be0d7602f6c",
"name": "Overview"
},
{
"parameters": {
"content": "### Step 1: Extract Data\nResource: **Document Extraction**\n\nConfigure the Document Extraction parameters below, then connect your credentials.",
"height": 160,
"width": 300,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
475,
100
],
"id": "9268c275-4ae6-4e58-bf98-1b51dbcbb45a",
"name": "Step 1 Note"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
250,
300
],
"id": "a5b6c7d8-e9f0-1234-abcd-345678901abc",
"name": "Manual Trigger"
},
{
"parameters": {
"resource": "documentExtraction",
"schemaInputMode": "rawJson",
"schemaJson": "{\"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\"}]}]}",
"files": {
"fileValues": [
{
"fileInputMode": "url",
"fileName": "resume.pdf",
"fileUrl": "https://example.com/resumes/resume.pdf"
}
]
}
},
"type": "n8n-nodes-iterationlayer.iterationLayer",
"typeVersion": 1,
"position": [
500,
300
],
"id": "b6c7d8e9-f0a1-2345-bcde-456789012bcd",
"name": "Extract Data",
"credentials": {
"iterationLayerApi": {
"id": "1",
"name": "Iteration Layer API"
}
}
}
],
"connections": {
"Manual Trigger": {
"main": [
[
{
"node": "Extract Data",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
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": []
}
}
}
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