# OPM FedScope Federal Workforce Scraper (`jungle_synthesizer/opm-fedscope-federal-workforce-scraper`) Actor Scrapes OPM FedScope federal workforce data from www.opm.gov. Returns aggregated federal employment statistics by agency, pay plan, grade, occupation, and location. Data is sourced from OPM's quarterly FedScope Employment Summary datasets -- no auth, no CAPTCHA, public data. - **URL**: https://apify.com/jungle\_synthesizer/opm-fedscope-federal-workforce-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Lead generation - **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 ## OPM FedScope Federal Workforce Scraper Scrapes federal workforce data from [OPM's FedScope datasets](https://www.opm.gov/data/datasets/Index.aspx) on www.opm.gov. Returns pre-aggregated employment statistics by agency, pay plan, grade, occupation, and duty location — sourced directly from OPM's quarterly ZIP releases, no auth required. --- ### OPM FedScope Federal Workforce Scraper Features - Extracts federal employment statistics from OPM's quarterly FedScope dataset ZIPs - Returns agency headcount, average salary, and average length of service across multiple dimensions - Filters by dataset type: employment summary, employment cubes, accessions, separations, or all - Streams large ZIP files line-by-line — no memory ceiling from loading 276 MB flat files - Covers data going back to FY 2005 (accessions/separations) and September 1998 (employment cubes) - No proxies required. OPM's public data page is accessible from any IP with a browser User-Agent. --- ### Who Uses Federal Workforce Data? - **Policy researchers** — Analyze agency staffing trends, grade distributions, and pay over time - **Government contractors** — Identify agencies by workforce size, occupation series, and location to target business development - **HR consultants** — Benchmark federal pay scales against private sector by grade, occupation, and region - **Journalists and watchdogs** — Track workforce reductions, hiring surges, or pay changes at specific agencies - **Data scientists** — Build longitudinal models of federal employment using quarterly snapshots back to the late 1990s --- ### How OPM FedScope Federal Workforce Scraper Works 1. Fetches the OPM data catalog page and parses all FedScope dataset entries — name, type, and ZIP download URL 2. Filters datasets by your selected type (employment summary, cubes, accessions, or separations) and caps at `max_datasets` ZIPs 3. Streams each ZIP file through an in-memory unzipper and parses the tab-delimited or pipe-delimited text files line by line 4. Maps each row to the output schema and saves records until `maxItems` is reached, then closes the stream cleanly --- ### Input ```json { "maxItems": 10, "dataset_type": "employment_summary", "max_datasets": 1 } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum number of records to return across all datasets fetched | | `dataset_type` | string | `employment_summary` | Type of FedScope dataset. Options: `employment_summary`, `employment_cube`, `accessions`, `separations`, `all` | | `max_datasets` | integer | 1 | Maximum number of dataset ZIPs to download. Default 1 for fast runs. Increase for historical data. | To pull a full history, set `max_datasets` higher and `dataset_type` to `employment_cube` for quarterly snapshots going back to 1998. ```json { "maxItems": 50000, "dataset_type": "employment_cube", "max_datasets": 10 } ``` *** ### OPM FedScope Federal Workforce Scraper Output Fields ```json { "dataset_name": "FedScope Employment Summary Data (March 2025)", "dataset_type": "employment_summary", "table_name": "Pay Plan and Grade", "datecode": "202503", "agency_code": "VA", "agency_name": "DEPARTMENT OF VETERANS AFFAIRS", "sub_agency_code": "VATA", "sub_agency_name": "VETERANS HEALTH ADMINISTRATION", "occupation_series": null, "occupation_title": null, "pay_plan": "GS", "grade": "12", "location_code": null, "location_state": null, "location_state_name": null, "employee_count": 5430, "average_salary": 95821, "average_los": 12.4, "source_url": "https://www.opm.gov/data/datasets/Files/753/bc88ce69-1bbe-406f-9441-3c5153014616.zip", "scraped_at": "2026-05-04T20:10:14.421Z" } ``` | Field | Type | Description | |-------|------|-------------| | `dataset_name` | string | Full dataset name from OPM (e.g. "FedScope Employment Summary Data (March 2025)") | | `dataset_type` | string | Dataset type: employment\_summary, employment\_cube, accessions, or separations | | `table_name` | string | Sub-table within the dataset ZIP (e.g. "By Agency and SubAgency") | | `datecode` | string | Reporting period in YYYYMM format (e.g. "202503" for March 2025) | | `agency_code` | string | Agency abbreviation code (e.g. "VA", "DD", "HE") | | `agency_name` | string | Full agency name (e.g. "DEPARTMENT OF VETERANS AFFAIRS") | | `sub_agency_code` | string | Sub-agency abbreviation code | | `sub_agency_name` | string | Full sub-agency name | | `occupation_series` | string | OPM occupation series code (e.g. "610" for Nurse) | | `occupation_title` | string | Occupation title (e.g. "NURSE") | | `pay_plan` | string | Pay plan code (e.g. "GS", "SES", "VN", "SV") | | `grade` | string | Grade within the pay plan (e.g. "12", "F", "3") | | `location_code` | string | OPM duty location code | | `location_state` | string | Two-letter state abbreviation (e.g. "CA", "TX") | | `location_state_name` | string | Full state name (e.g. "CALIFORNIA") | | `employee_count` | integer | Number of employees matching these dimensions | | `average_salary` | number | Average annual salary in USD | | `average_los` | number | Average length of service in years | | `source_url` | string | URL to the source ZIP file on www.opm.gov | | `scraped_at` | string | ISO timestamp when this record was scraped | Not every field is populated in every record — Employment Summary tables are pre-aggregated along specific dimensions, so a "By Pay Plan and Grade" table won't have location or occupation data. *** ### 🔍 FAQ #### How do I scrape federal employment data from OPM? OPM FedScope Federal Workforce Scraper fetches from `www.opm.gov/data/datasets/`, not the newer `data.opm.gov` portal (which requires a live browser session to generate download URLs). The legacy page has direct static ZIP links — cleaner, faster, and no JavaScript required. #### How much does it cost to run? Each actor start costs $0.10. Each record costs $0.001. A typical run with `max_datasets: 1` and `maxItems: 10000` costs around $10.10 — which is a lot cheaper than building a federal payroll database from scratch. #### What's the difference between employment\_summary and employment\_cube? Employment summary datasets are pre-aggregated tables (by agency, by pay plan/grade, by duty location, etc.) and run fast — one ZIP contains about 10 tables with tens of thousands of rows total. Employment cubes are quarterly raw snapshots with more granularity going back to September 1998. Use summary for current state; use cubes for longitudinal analysis. #### Does OPM FedScope Federal Workforce Scraper need proxies? It does not. OPM's data download page is accessible from Apify datacenter IPs with a standard browser User-Agent. No residential proxy, no CAPTCHA, no session required. #### Can I pull historical data across multiple quarters? Set `max_datasets` to however many quarters you need and `dataset_type` to `employment_cube`. The most recent datasets appear first in the catalog, so `max_datasets: 4` gives you roughly the last year of quarterly snapshots. *** ### Need More Features? Need custom field mappings, accessions/separations analysis, or a different OPM data product? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use OPM FedScope Federal Workforce Scraper? - **No auth, no CAPTCHA** — OPM's legacy data page is fully public with direct ZIP links, so the actor just downloads and parses - **Streaming ZIP parser** — handles files up to 276 MB uncompressed without loading them into memory, which means it doesn't fall over on large historical pulls - **20+ years of history available** — employment cube datasets go back to September 1998, accessions and separations to FY 2005 # 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. ## `maxItems` (type: `integer`): Maximum number of records to return across all datasets fetched ## `dataset_type` (type: `string`): Type of FedScope dataset to fetch. 'employment\_summary' returns aggregated tables by agency/grade/location. 'employment\_cube' returns quarterly raw snapshots. 'accessions' returns new hire data. 'separations' returns departure data. 'all' fetches all types. ## `max_datasets` (type: `integer`): Maximum number of dataset ZIPs to download and parse (most recent first). Default 1 for fast runs. Increase for historical data. ## 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...", "maxItems": 10, "dataset_type": "employment_summary", "max_datasets": 1 } ``` # 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...", "maxItems": 10, "dataset_type": "employment_summary", "max_datasets": 1 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/opm-fedscope-federal-workforce-scraper").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...", "maxItems": 10, "dataset_type": "employment_summary", "max_datasets": 1, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/opm-fedscope-federal-workforce-scraper").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...", "maxItems": 10, "dataset_type": "employment_summary", "max_datasets": 1 }' | apify call jungle_synthesizer/opm-fedscope-federal-workforce-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/opm-fedscope-federal-workforce-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OPM FedScope Federal Workforce Scraper", "description": "Scrapes OPM FedScope federal workforce data from www.opm.gov. Returns aggregated federal employment statistics by agency, pay plan, grade, occupation, and location. Data is sourced from OPM's quarterly FedScope Employment Summary datasets -- no auth, no CAPTCHA, public data.\n", "version": "0.1", "x-build-id": "ifRmCB6jwb3SiSisS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~opm-fedscope-federal-workforce-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-opm-fedscope-federal-workforce-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~opm-fedscope-federal-workforce-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-opm-fedscope-federal-workforce-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~opm-fedscope-federal-workforce-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-opm-fedscope-federal-workforce-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "maxItems" ], "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." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to return across all datasets fetched", "default": 10 }, "dataset_type": { "title": "Dataset Type", "enum": [ "employment_summary", "employment_cube", "accessions", "separations", "all" ], "type": "string", "description": "Type of FedScope dataset to fetch. 'employment_summary' returns aggregated tables by agency/grade/location. 'employment_cube' returns quarterly raw snapshots. 'accessions' returns new hire data. 'separations' returns departure data. 'all' fetches all types.", "default": "employment_summary" }, "max_datasets": { "title": "Max Datasets", "type": "integer", "description": "Maximum number of dataset ZIPs to download and parse (most recent first). Default 1 for fast runs. Increase for historical data.", "default": 1 } } }, "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 } } } } } } } } } } ```