# AI Resume / CV Optimizer (`jungle_synthesizer/ai-resume-cv-optimizer`) Actor Paste your CV and a job description to get instant AI-powered feedback. Scores keyword alignment, identifies skill gaps, highlights structural issues, and returns ready-to-use rewrite suggestions. Supports GPT-4o-mini and GPT-4o via your OpenAI API key. - **URL**: https://apify.com/jungle\_synthesizer/ai-resume-cv-optimizer.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Jobs, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## AI Resume / CV Optimizer Paste your CV and a job description to get instant AI-powered feedback. Scores keyword alignment, identifies skill gaps, highlights structural issues, and returns ready-to-use rewrite suggestions. Supports GPT-4o-mini and GPT-4o via your OpenAI API key. ### What it does This actor takes a CV / resume and a job description, then uses an LLM to produce a structured analysis: - **Match score** (0–100) — Overall fit based on keyword and skills alignment - **Verdict** — One-sentence screening assessment - **Strengths** — What the CV already does well for this role - **Skill gaps** — Required skills or keywords missing from the CV - **Structure feedback** — Layout, formatting, and presentation notes - **Action items** — Prioritized list of improvements to make - **Rewrite suggestions** — Ready-to-use text for weak or missing sections ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `cvText` | string | Yes | Full text of the CV/resume (plain text or Markdown) | | `jobDescription` | string | Yes | Full job description text including requirements | | `openAiApiKey` | string | Yes | Your OpenAI API key (`sk-...`). Get one at platform.openai.com | | `model` | string | No | `gpt-4o-mini` (default, fast) or `gpt-4o` (thorough) | ### Output Each run produces a single dataset record: ```json { "matchScore": 72, "verdict": "Strong candidate — TypeScript and Node.js match well but cloud platform experience is missing.", "strengths": [ "TypeScript and Node.js listed in skills match required stack", "5+ years of experience meets the seniority requirement", "Docker experience aligns with containerization nice-to-have" ], "skillGaps": [ "No mention of AWS, GCP, or Azure — cloud platform required", "CI/CD pipeline experience not demonstrated with specific tools", "NoSQL database experience not listed" ], "structureFeedback": [ "Add a concise professional summary at the top", "Quantify achievements — replace 'improved performance' with specific metrics" ], "actionItems": [ "Add a cloud platform certification or project to address the AWS/GCP gap", "List CI/CD tools (GitHub Actions, Jenkins) in the skills section", "Add MongoDB or Redis to database skills" ], "rewriteSuggestions": [ "Skills: 'PostgreSQL, Redis' → expand to 'PostgreSQL (primary), Redis (caching), MongoDB (exploratory)'" ], "model": "gpt-4o-mini", "analyzedAt": "2026-05-27T10:00:00.000Z" } ```` ### API key This actor requires your own OpenAI API key. GPT-4o-mini costs approximately $0.15 per million input tokens — a typical CV + JD analysis uses under 2,000 tokens, making each run a fraction of a cent. Get an API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys). ### Notes - CV text can be plain text, Markdown, or copy-pasted from a PDF - The actor runs a single LLM call per run — no storage or external requests beyond OpenAI - Results vary slightly between runs due to model temperature (set to 0.3 for consistency) # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `cvText` (type: `string`): Paste the full text of the CV or resume to analyze. Plain text preferred; Markdown is accepted. ## `jobDescription` (type: `string`): Paste the full job description text. Include requirements, responsibilities, and any preferred qualifications. ## `openAiApiKey` (type: `string`): Your OpenAI API key (sk-...). Required to run the LLM analysis. Get yours at https://platform.openai.com/api-keys. ## `model` (type: `string`): OpenAI model to use for analysis. GPT-4o-mini is fast and cost-effective; GPT-4o is more thorough. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "cvText": "John Smith\nSoftware Engineer | 5 years experience\n\nExperience:\n- Software Engineer at Acme Corp (2020–present): Built REST APIs with Node.js, led team of 3.\n- Junior Dev at StartupXYZ (2018–2020): Frontend React work, improved page load by 40%.\n\nSkills: JavaScript, TypeScript, Node.js, React, PostgreSQL, Docker\nEducation: B.Sc Computer Science, State University 2018", "jobDescription": "Senior Full-Stack Engineer at TechCorp\n\nWe are looking for a Senior Full-Stack Engineer with 5+ years of experience.\n\nRequired:\n- 5+ years of software engineering experience\n- Proficiency in TypeScript, Node.js, and React\n- Experience with cloud platforms (AWS, GCP, or Azure)\n- Strong knowledge of SQL and NoSQL databases\n- Experience with CI/CD pipelines and DevOps practices\n\nNice to have:\n- Kubernetes / Docker experience\n- Experience mentoring junior engineers\n- GraphQL knowledge", "model": "gpt-4o-mini" } ``` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "cvText": `John Smith Software Engineer | 5 years experience Experience: - Software Engineer at Acme Corp (2020–present): Built REST APIs with Node.js, led team of 3. - Junior Dev at StartupXYZ (2018–2020): Frontend React work, improved page load by 40%. Skills: JavaScript, TypeScript, Node.js, React, PostgreSQL, Docker Education: B.Sc Computer Science, State University 2018`, "jobDescription": `Senior Full-Stack Engineer at TechCorp We are looking for a Senior Full-Stack Engineer with 5+ years of experience. Required: - 5+ years of software engineering experience - Proficiency in TypeScript, Node.js, and React - Experience with cloud platforms (AWS, GCP, or Azure) - Strong knowledge of SQL and NoSQL databases - Experience with CI/CD pipelines and DevOps practices Nice to have: - Kubernetes / Docker experience - Experience mentoring junior engineers - GraphQL knowledge`, "model": "gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/ai-resume-cv-optimizer").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "cvText": """John Smith Software Engineer | 5 years experience Experience: - Software Engineer at Acme Corp (2020–present): Built REST APIs with Node.js, led team of 3. - Junior Dev at StartupXYZ (2018–2020): Frontend React work, improved page load by 40%. Skills: JavaScript, TypeScript, Node.js, React, PostgreSQL, Docker Education: B.Sc Computer Science, State University 2018""", "jobDescription": """Senior Full-Stack Engineer at TechCorp We are looking for a Senior Full-Stack Engineer with 5+ years of experience. Required: - 5+ years of software engineering experience - Proficiency in TypeScript, Node.js, and React - Experience with cloud platforms (AWS, GCP, or Azure) - Strong knowledge of SQL and NoSQL databases - Experience with CI/CD pipelines and DevOps practices Nice to have: - Kubernetes / Docker experience - Experience mentoring junior engineers - GraphQL knowledge""", "model": "gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/ai-resume-cv-optimizer").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "cvText": "John Smith\\nSoftware Engineer | 5 years experience\\n\\nExperience:\\n- Software Engineer at Acme Corp (2020–present): Built REST APIs with Node.js, led team of 3.\\n- Junior Dev at StartupXYZ (2018–2020): Frontend React work, improved page load by 40%.\\n\\nSkills: JavaScript, TypeScript, Node.js, React, PostgreSQL, Docker\\nEducation: B.Sc Computer Science, State University 2018", "jobDescription": "Senior Full-Stack Engineer at TechCorp\\n\\nWe are looking for a Senior Full-Stack Engineer with 5+ years of experience.\\n\\nRequired:\\n- 5+ years of software engineering experience\\n- Proficiency in TypeScript, Node.js, and React\\n- Experience with cloud platforms (AWS, GCP, or Azure)\\n- Strong knowledge of SQL and NoSQL databases\\n- Experience with CI/CD pipelines and DevOps practices\\n\\nNice to have:\\n- Kubernetes / Docker experience\\n- Experience mentoring junior engineers\\n- GraphQL knowledge", "model": "gpt-4o-mini" }' | apify call jungle_synthesizer/ai-resume-cv-optimizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/ai-resume-cv-optimizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Resume / CV Optimizer", "description": "Paste your CV and a job description to get instant AI-powered feedback. Scores keyword alignment, identifies skill gaps, highlights structural issues, and returns ready-to-use rewrite suggestions. Supports GPT-4o-mini and GPT-4o via your OpenAI API key.", "version": "0.1", "x-build-id": "Eycivc5crFCFziXnj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~ai-resume-cv-optimizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-ai-resume-cv-optimizer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~ai-resume-cv-optimizer/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-ai-resume-cv-optimizer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~ai-resume-cv-optimizer/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-ai-resume-cv-optimizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "cvText", "jobDescription" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "cvText": { "title": "CV / Resume Text", "type": "string", "description": "Paste the full text of the CV or resume to analyze. Plain text preferred; Markdown is accepted." }, "jobDescription": { "title": "Job Description", "type": "string", "description": "Paste the full job description text. Include requirements, responsibilities, and any preferred qualifications." }, "openAiApiKey": { "title": "OpenAI API Key", "type": "string", "description": "Your OpenAI API key (sk-...). Required to run the LLM analysis. Get yours at https://platform.openai.com/api-keys." }, "model": { "title": "Model", "enum": [ "gpt-4o-mini", "gpt-4o" ], "type": "string", "description": "OpenAI model to use for analysis. GPT-4o-mini is fast and cost-effective; GPT-4o is more thorough.", "default": "gpt-4o-mini" } } }, "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 } } } } } } } } } } ```