# Linkedin Job Application Copilot (`immense_greenery/linkedin-application-copilot`) Actor Score LinkedIn job listings against your profile, generate AI-powered application packs with cover note drafts, fit scores, and next steps. So you apply smarter, not more. - **URL**: https://apify.com/immense\_greenery/linkedin-application-copilot.md - **Developed by:** [Adam Tokar](https://apify.com/immense_greenery) (community) - **Categories:** AI, Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 application pack results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## LinkedIn Application Copilot Stop applying to jobs blindly. This Actor scores LinkedIn job listings against your profile, then generates a full application pack for each role — fit score, cover note draft, resume emphasis points, and guardrails — so you apply to the right jobs, better prepared. --- ### What it does - Searches LinkedIn jobs by keyword, location, date posted, and workplace type (remote / hybrid / on-site) - Runs multiple searches in a single run (e.g. "Product Designer in NYC" + "Growth Marketer in London") - Scores each role 0–100 against your target roles, core skills, and avoid-keywords - Generates a per-job application pack: resume emphasis, cover note draft, headline, guardrails, and next steps - Filters out low-fit jobs automatically (configurable minimum score) - Exports clean structured data to JSON, CSV, Google Sheets, or a downstream tracker ### What it does NOT do LinkedIn postings are **prepared for review, not auto-submitted.** You review every application pack and apply from your own account. This is intentional — governed, human-in-the-loop job searching. --- ### Works great with Feed job records directly from these popular scrapers: - [bebity/linkedin-jobs-scraper](https://apify.com/bebity/linkedin-jobs-scraper) - [curious_coder/linkedin-jobs-scraper](https://apify.com/curious_coder/linkedin-jobs-scraper) - Any ATS export or CSV of job postings via the `jobs` input field --- ### Example input ```json { "discoverLinkedInJobs": true, "searches": [ { "keywords": "Product Designer", "location": "United States", "postedWithin": "past-week", "workplaceType": "remote" } ], "candidateProfile": { "targetRoles": ["Product Designer", "UX Designer"], "coreSkills": ["figma", "design systems", "user research"], "avoidKeywords": ["internship", "unpaid"] }, "minScore": 70, "maxJobs": 25 } ```` ### Example output ```json { "title": "Senior Product Designer", "company": "ExampleCloud", "fitScore": 84, "fitSignals": ["figma", "design systems", "remote-first team"], "recommendation": "Strong match — apply this week", "submissionMode": "manual_review", "requiresHumanApproval": true, "applicationPack": { "resumeEmphasis": ["Led design system overhaul", "Figma component library"], "headline": "Product Designer with 3+ years in scalable design systems", "coverNoteDraft": "I'm excited about this role because...", "guardrails": ["Role requires 5 YOE — address directly in cover note"] }, "nextSteps": ["Tailor resume header", "Reference their design system work in cover note"] } ``` *** ### Pricing Charged at **$0.05 per application pack generated** — you only pay for jobs that pass your minimum fit score threshold. # Actor input Schema ## `discoverLinkedInJobs` (type: `boolean`): When enabled, the Actor searches public LinkedIn job listings from the buyer-defined search fields below before preparing application packs. ## `searches` (type: `array`): Buyer-defined LinkedIn searches. Add one or more target roles, locations, and filters. These replace the old one-person defaults. ## `searchKeywords` (type: `string`): Optional single-search shortcut. Use this instead of Job searches if the buyer only wants one role query. ## `searchLocation` (type: `string`): Optional single-search shortcut location, for example United States, London, Remote, or Berlin. ## `postedWithin` (type: `string`): Optional LinkedIn date filter. ## `workplaceType` (type: `string`): Optional LinkedIn workplace filter. ## `maxDiscoveryJobs` (type: `integer`): Maximum number of public LinkedIn jobs to discover before scoring. ## `fetchJobDescriptions` (type: `boolean`): Fetch each LinkedIn job detail page to improve scoring with description text. Disable for faster, cheaper runs. ## `jobs` (type: `array`): Optional job records to score and prepare. Use this for API runs, CSV-derived jobs, ATS postings, or records from another Actor. ## `candidateProfile` (type: `object`): Buyer profile used for scoring and apply-pack language. Set target roles, desired skills, and optional keywords to avoid. ## `minScore` (type: `integer`): Minimum fit score required to prepare an application pack. ## `maxJobs` (type: `integer`): Maximum number of jobs to process in one Actor run. ## `syncToNetlify` (type: `boolean`): When enabled, prepared jobs are sent to your Netlify tracker intake endpoint as manual-source records. ## `netlifySiteUrl` (type: `string`): Tracker site URL, for example https://precious-cupcake-3792b5.netlify.app. Required only when syncToNetlify is enabled. ## Actor input object example ```json { "discoverLinkedInJobs": true, "searches": [ { "keywords": "Product Designer", "location": "United States", "postedWithin": "past-week", "workplaceType": "remote" }, { "keywords": "Growth Marketer", "location": "London", "postedWithin": "past-month", "workplaceType": "hybrid" } ], "searchKeywords": "Product Designer", "searchLocation": "United States", "postedWithin": "past-week", "workplaceType": "any", "maxDiscoveryJobs": 25, "fetchJobDescriptions": true, "jobs": [ { "title": "Product Designer", "company": "ExampleCo", "location": "United States", "url": "https://www.linkedin.com/jobs/view/1234567890/", "description": "Own Figma prototyping, user research, design systems, and product collaboration." } ], "candidateProfile": { "targetRoles": [ "Product Designer" ], "coreSkills": [ "figma", "design systems", "user research" ], "avoidKeywords": [ "internship", "unpaid" ] }, "minScore": 70, "maxJobs": 50, "syncToNetlify": false } ``` # Actor output Schema ## `applicationPacks` (type: `string`): Ranked job opportunities with fit score, recommendation, review status, and application-pack drafts. ## `summary` (type: `string`): Counts of total jobs, prepared application packs, skipped jobs, human-review requirements, and Netlify sync status. # 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 = { "searches": [ { "keywords": "Product Designer", "location": "United States", "postedWithin": "past-week", "workplaceType": "remote" }, { "keywords": "Growth Marketer", "location": "London", "postedWithin": "past-month", "workplaceType": "hybrid" } ], "searchKeywords": "Product Designer", "searchLocation": "United States", "jobs": [ { "title": "Product Designer", "company": "ExampleCo", "location": "United States", "url": "https://www.linkedin.com/jobs/view/1234567890/", "description": "Own Figma prototyping, user research, design systems, and product collaboration." } ], "candidateProfile": { "targetRoles": [ "Product Designer" ], "coreSkills": [ "figma", "design systems", "user research" ], "avoidKeywords": [ "internship", "unpaid" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("immense_greenery/linkedin-application-copilot").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 = { "searches": [ { "keywords": "Product Designer", "location": "United States", "postedWithin": "past-week", "workplaceType": "remote", }, { "keywords": "Growth Marketer", "location": "London", "postedWithin": "past-month", "workplaceType": "hybrid", }, ], "searchKeywords": "Product Designer", "searchLocation": "United States", "jobs": [{ "title": "Product Designer", "company": "ExampleCo", "location": "United States", "url": "https://www.linkedin.com/jobs/view/1234567890/", "description": "Own Figma prototyping, user research, design systems, and product collaboration.", }], "candidateProfile": { "targetRoles": ["Product Designer"], "coreSkills": [ "figma", "design systems", "user research", ], "avoidKeywords": [ "internship", "unpaid", ], }, } # Run the Actor and wait for it to finish run = client.actor("immense_greenery/linkedin-application-copilot").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 '{ "searches": [ { "keywords": "Product Designer", "location": "United States", "postedWithin": "past-week", "workplaceType": "remote" }, { "keywords": "Growth Marketer", "location": "London", "postedWithin": "past-month", "workplaceType": "hybrid" } ], "searchKeywords": "Product Designer", "searchLocation": "United States", "jobs": [ { "title": "Product Designer", "company": "ExampleCo", "location": "United States", "url": "https://www.linkedin.com/jobs/view/1234567890/", "description": "Own Figma prototyping, user research, design systems, and product collaboration." } ], "candidateProfile": { "targetRoles": [ "Product Designer" ], "coreSkills": [ "figma", "design systems", "user research" ], "avoidKeywords": [ "internship", "unpaid" ] } }' | apify call immense_greenery/linkedin-application-copilot --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=immense_greenery/linkedin-application-copilot", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Job Application Copilot", "description": "Score LinkedIn job listings against your profile, generate AI-powered application packs with cover note drafts, fit scores, and next steps. So you apply smarter, not more.", "version": "0.1", "x-build-id": "zyE2vkdQfHS7t5KuX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/immense_greenery~linkedin-application-copilot/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-immense_greenery-linkedin-application-copilot", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/immense_greenery~linkedin-application-copilot/runs": { "post": { "operationId": "runs-sync-immense_greenery-linkedin-application-copilot", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/immense_greenery~linkedin-application-copilot/run-sync": { "post": { "operationId": "run-sync-immense_greenery-linkedin-application-copilot", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "discoverLinkedInJobs": { "title": "Discover LinkedIn jobs automatically", "type": "boolean", "description": "When enabled, the Actor searches public LinkedIn job listings from the buyer-defined search fields below before preparing application packs.", "default": true }, "searches": { "title": "Job searches", "type": "array", "description": "Buyer-defined LinkedIn searches. Add one or more target roles, locations, and filters. These replace the old one-person defaults." }, "searchKeywords": { "title": "Single search keywords", "type": "string", "description": "Optional single-search shortcut. Use this instead of Job searches if the buyer only wants one role query." }, "searchLocation": { "title": "Single search location", "type": "string", "description": "Optional single-search shortcut location, for example United States, London, Remote, or Berlin." }, "postedWithin": { "title": "Posted within", "enum": [ "any", "past-24h", "past-week", "past-month" ], "type": "string", "description": "Optional LinkedIn date filter.", "default": "past-week" }, "workplaceType": { "title": "Workplace type", "enum": [ "any", "remote", "hybrid", "onsite" ], "type": "string", "description": "Optional LinkedIn workplace filter.", "default": "any" }, "maxDiscoveryJobs": { "title": "Max jobs to discover", "minimum": 1, "maximum": 250, "type": "integer", "description": "Maximum number of public LinkedIn jobs to discover before scoring.", "default": 25 }, "fetchJobDescriptions": { "title": "Fetch job descriptions", "type": "boolean", "description": "Fetch each LinkedIn job detail page to improve scoring with description text. Disable for faster, cheaper runs.", "default": true }, "jobs": { "title": "Optional pasted jobs", "type": "array", "description": "Optional job records to score and prepare. Use this for API runs, CSV-derived jobs, ATS postings, or records from another Actor." }, "candidateProfile": { "title": "Candidate profile", "type": "object", "description": "Buyer profile used for scoring and apply-pack language. Set target roles, desired skills, and optional keywords to avoid." }, "minScore": { "title": "Minimum score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Minimum fit score required to prepare an application pack.", "default": 70 }, "maxJobs": { "title": "Max jobs", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of jobs to process in one Actor run.", "default": 50 }, "syncToNetlify": { "title": "Sync prepared jobs to Netlify tracker", "type": "boolean", "description": "When enabled, prepared jobs are sent to your Netlify tracker intake endpoint as manual-source records.", "default": false }, "netlifySiteUrl": { "title": "Netlify site URL", "type": "string", "description": "Tracker site URL, for example https://precious-cupcake-3792b5.netlify.app. Required only when syncToNetlify is enabled." } } }, "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 } } } } } } } } } } ```