# Resume Parser - Extract Skills & Experience (`ntriqpro/resume-parser`) Actor Automatically read resumes and PDFs. Extract contact info, skills, work history, and education. - **URL**: https://apify.com/ntriqpro/resume-parser.md - **Developed by:** [daehwan kim](https://apify.com/ntriqpro) (community) - **Categories:** Business, AI - **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $100.00 / 1,000 resume parseds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Resume Parser & Skill Extractor Parse PDF resumes to extract contact info, technical skills, experience years, and education. Optional job description matching score for candidate screening. Perfect for HR teams and recruiting platforms. ### Features - **Contact Information Extraction**: Automatically detects candidate name, email, and phone number from resume text - **Technical Skills Detection**: Identifies 80+ tech keywords across 9 categories (languages, frontend, backend, database, cloud, devops, mobile, AI/ML, tools) - **Experience Estimation**: Estimates years of professional experience from date patterns in resume - **Education Extraction**: Detects Bachelor's, Master's, PhD, and Associate degrees with fields of study - **Job Description Matching**: Optional skill match scoring against provided job description (0-100%) - **PDF Metadata**: Extracts page count and word count for resume assessment - **Comprehensive Disclaimer**: Includes GDPR/CCPA compliance notice for responsible use ### Extracted Fields | Field | Type | Description | |-------|------|-------------| | `resumeUrl` | string | Input PDF URL | | `candidateName` | string | Extracted candidate full name | | `email` | string | Email address (normalized to lowercase) | | `phone` | string | Phone number in original format | | `skills` | object | Skills grouped by category (languages, frontend, backend, etc.) | | `allSkillsList` | array | Flat list of all detected skills | | `skillCount` | integer | Total number of unique skills found | | `estimatedExperienceYears` | integer | Years since first detected work date | | `education` | array | Array of degrees with level and field | | `pageCount` | integer | Number of pages in PDF | | `wordCount` | integer | Total words in resume text | | `jobFitScore` | integer | Match percentage (0-100%) if job description provided | | `jobFitScoreNote` | string | Explanation of job fit calculation | | `disclaimer` | string | Legal/compliance disclaimer text | ### Input ```json { "resumeUrl": "https://example.com/resume.pdf", "jobDescription": "We're looking for a React developer with 5+ years experience..." } ```` | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `resumeUrl` | string | YES | Direct URL to PDF resume file | | `jobDescription` | string | NO | Job description text for skill matching (0-100% score calculated if provided) | ### Output Actor pushes one item per resume to the default dataset with all extracted fields. **Example Output:** ```json { "resumeUrl": "https://example.com/resume.pdf", "candidateName": "John Smith", "email": "john@example.com", "phone": "+1 555-123-4567", "skills": { "languages": ["javascript", "typescript", "python"], "frontend": ["react", "nextjs"], "backend": ["nodejs", "express"], "database": ["postgresql"] }, "allSkillsList": ["javascript", "typescript", "python", "react", "nextjs", "nodejs", "express", "postgresql"], "skillCount": 8, "estimatedExperienceYears": 6, "education": [ { "level": "Bachelor", "field": "Computer Science" } ], "pageCount": 2, "wordCount": 847, "jobFitScore": 75, "jobFitScoreNote": "75% of JD tech keywords found in resume", "disclaimer": "..." } ``` ### Pricing **$0.10 per resume parsed** (Pay-per-event model) Billing event: `resume-parsed` ### Usage Example ```bash curl -X POST https://api.apify.com/v2/acts/{actor-id}/runs \ -H "Authorization: Bearer {api-token}" \ -H "Content-Type: application/json" \ -d '{ "resumeUrl": "https://example.com/resume.pdf", "jobDescription": "Senior Full Stack Engineer with 7+ years experience in React and Node.js" }' ``` ### Supported Skill Categories - **Languages**: JavaScript, TypeScript, Python, Java, C++, C#, Go, Rust, Ruby, PHP, Swift, Kotlin, Scala, R, MATLAB, Perl, Haskell, Elixir, Dart, Lua - **Frontend**: React, Vue, Angular, Svelte, Next.js, Nuxt.js, HTML, CSS, Sass, Tailwind, Bootstrap, Webpack, Vite, Redux, Mobx, GraphQL - **Backend**: Node.js, Express, FastAPI, Django, Flask, Spring, Rails, Laravel, ASP.NET, Nest.js, Hapi, Koa, Gin, Echo, Fiber - **Database**: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, SQLite, Oracle, MSSQL, DynamoDB, Cassandra, Neo4j, Firebase, Supabase - **Cloud**: AWS, Azure, GCP, Google Cloud, Heroku, Vercel, Netlify, Cloudflare, DigitalOcean, Linode - **DevOps**: Docker, Kubernetes, Terraform, Ansible, Jenkins, GitHub Actions, GitLab CI, CircleCI, Helm, Prometheus, Grafana - **Mobile**: React Native, Flutter, Swift, Kotlin, iOS, Android, Expo - **AI/ML**: PyTorch, TensorFlow, Scikit-learn, Keras, Pandas, NumPy, OpenCV, Hugging Face, LangChain, OpenAI, LLM, Machine Learning, Deep Learning, NLP, Computer Vision - **Tools**: Git, Jira, Confluence, Slack, Figma, Postman, Swagger, Linux, Bash, Vim ### Limitations - **Scanned/Image PDFs**: Not supported. Actor requires machine-readable text-based PDFs. Scanned resumes will return minimal results. - **Encrypted PDFs**: Password-protected PDFs cannot be parsed. - **Non-English Resumes**: Skill detection optimized for English keywords. Other languages have limited accuracy. - **Uncommon Formats**: Highly non-standard resume layouts may have extraction accuracy issues. - **Manual Review Required**: Results should always be reviewed by human recruiters. Automated parsing is not error-proof. ### Disclaimer **IMPORTANT LEGAL & COMPLIANCE NOTICE** This tool extracts information from PDF resume files using open source text parsing libraries. Extraction accuracy depends on the PDF structure, formatting, and encoding. Scanned or image-based PDFs will have very limited accuracy. Results may miss information or contain errors. This tool is intended to assist human reviewers, not replace them. Do not make final hiring decisions based solely on automated parsing results. **Data Protection & Privacy Compliance:** The operator is not responsible for hiring outcomes, data accuracy, or privacy compliance. Ensure all resume processing complies with applicable employment laws and data protection regulations: - **GDPR** (EU): Obtain explicit consent before processing personal data - **CCPA** (California): Provide privacy notices and honor data access/deletion requests - **ADA** (USA): Ensure fair assessment of candidates with disabilities - **Other jurisdictions**: Verify local employment and data protection laws **Responsible Use:** - Obtain candidate consent before parsing their resume - Use results only to supplement human review, not replace it - Maintain secure storage of extracted personal data - Implement data retention and deletion policies - Do not use for discriminatory screening ### License This actor uses [pdf-parse](https://github.com/modularcode/pdf-parse) (MIT License) for PDF parsing. # Actor input Schema ## `resumeUrl` (type: `string`): Direct URL to a publicly accessible PDF resume. Replace the example URL with your own resume PDF link. Must be text-based (not scanned image). ## `jobDescription` (type: `string`): Paste the job description text to calculate candidate skill match score ## Actor input object example ```json { "resumeUrl": "https://freetestdata.com/wp-content/uploads/2021/09/Free_Test_Data_100KB_PDF.pdf" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "resumeUrl": "https://freetestdata.com/wp-content/uploads/2021/09/Free_Test_Data_100KB_PDF.pdf", "jobDescription": "" }; // Run the Actor and wait for it to finish const run = await client.actor("ntriqpro/resume-parser").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "resumeUrl": "https://freetestdata.com/wp-content/uploads/2021/09/Free_Test_Data_100KB_PDF.pdf", "jobDescription": "", } # Run the Actor and wait for it to finish run = client.actor("ntriqpro/resume-parser").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "resumeUrl": "https://freetestdata.com/wp-content/uploads/2021/09/Free_Test_Data_100KB_PDF.pdf", "jobDescription": "" }' | apify call ntriqpro/resume-parser --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ntriqpro/resume-parser", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Resume Parser - Extract Skills & Experience", "description": "Automatically read resumes and PDFs. Extract contact info, skills, work history, and education.", "version": "1.0", "x-build-id": "1RErcqrCEVbsRGhiv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ntriqpro~resume-parser/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ntriqpro-resume-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ntriqpro~resume-parser/runs": { "post": { "operationId": "runs-sync-ntriqpro-resume-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ntriqpro~resume-parser/run-sync": { "post": { "operationId": "run-sync-ntriqpro-resume-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "resumeUrl" ], "properties": { "resumeUrl": { "title": "Resume PDF URL", "type": "string", "description": "Direct URL to a publicly accessible PDF resume. Replace the example URL with your own resume PDF link. Must be text-based (not scanned image)." }, "jobDescription": { "title": "Job Description (optional)", "type": "string", "description": "Paste the job description text to calculate candidate skill match score" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```