# GovCon Recompete & Incumbent Watch (`taroyamada/govcon-recompete-incumbent-watch-scraper`) Actor Unofficially watch public federal award data for recompete windows and incumbent contractor signals. - **URL**: https://apify.com/taroyamada/govcon-recompete-incumbent-watch-scraper.md - **Developed by:** [åĪŠéƒŽ åąąį”°](https://apify.com/taroyamada) (community) - **Categories:** Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $80.00 / 1,000 govcon recompete candidates 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 ## GovCon Recompete & Incumbent Watch Government contractors, capture teams, and BD agencies use this actor to watch public federal award records for recompete windows. Provide agencies, keywords, NAICS/PSC codes, recipient names, date windows, and minimum award amounts. The actor returns source-linked incumbent map, recompete candidate, change, report, and optional award rows. ### Store Quickstart Run with `dryRun=false` and public award search filters. ```json { "awardSearches": [ { "id": "cloud-watch", "keywords": ["cloud", "modernization"], "naicsCodes": ["541512"], "daysUntilEnd": 365, "minimumAwardAmount": 1000000 } ], "emitBaselineSummary": true, "generateReport": true, "emitUnchanged": false, "dryRun": false } ```` ### Input Examples #### NAICS watch ```json { "awardSearches": [ { "id": "it-services", "naicsCodes": ["541512"], "daysUntilEnd": 540, "minimumAwardAmount": 500000 } ], "dryRun": false } ``` #### Incumbent-name watch ```json { "awardSearches": [ { "id": "incumbent-watch", "recipientNames": ["Example Federal LLC"], "keywords": ["help desk", "cloud"], "daysUntilEnd": 365 } ], "generateReport": true, "dryRun": false } ``` #### Optional award observation rows ```json { "awardSearches": [ { "id": "cyber-watch", "keywords": ["cybersecurity"], "pscCodes": ["D310"], "daysUntilEnd": 730 } ], "emitAwardRows": true, "dryRun": false } ``` ### Sample Output ```json { "actorName": "govcon-recompete-incumbent-watch-scraper", "rowType": "govcon_recompete_candidate", "billingEventName": "govcon-recompete-candidate", "searchId": "sample-it-services", "incumbentName": "Sample Systems LLC", "sourceUrl": "https://www.usaspending.gov/award/sample" } ``` ### Output Fields - `rowType`: `govcon_incumbent_map_summary`, `govcon_recompete_candidate`, `govcon_incumbent_change`, `govcon_incumbent_award_observed`, or `govcon_recompete_watch_report`. - `searchId`: input search identifier. - `award`: normalized public award fields. - `incumbentName`: recipient name observed in public award data. - `daysUntilEnd`: computed calendar distance to the public award end date. - `sourceUrl`: public USAspending award URL. ### Pricing And No-Change Runs - `govcon-recompete-candidate`: $0.080 per source-linked recompete candidate. - `govcon-incumbent-map-generated`: $0.100 per incumbent map summary. - `govcon-incumbent-change-detected`: $0.060 per incumbent or award-content change signal. - `govcon-incumbent-award-observed`: $0.004 per optional award observation row. - `govcon-recompete-watch-report`: $6.000 per portfolio report. When `emitUnchanged=false`, repeated unchanged runs emit zero dataset rows and zero charges after state is saved. ### Compliance Guardrails - Official public USAspending data only. - `fixturePath` is dry-run/test-only and is rejected for public non-dry runs. - This is an unofficial tool and is not affiliated with USAspending, SAM.gov, GSA, or any government agency. - No SAM login, private notices, government approval wording, source-selection inference, procurement advice, legal advice, protest advice, or bid/no-bid recommendation. - Rows are source-linked public facts for human review. ### Bundle Paths - GovCon Capture: run a low-cost recompete candidate scan first, then generate `govcon-recompete-watch-report` for capture planning review. - Pair with [Procurement Intel Actor](https://apify.com/taroyamada/procurement-intel-actor) for broader public procurement signals. - Pair with [SEC EDGAR Material Disclosure Watch Scraper](https://apify.com/taroyamada/sec-edgar-material-disclosure-watch-scraper) for public company disclosure watch reports. ### See Also - [Procurement Intel Actor](https://apify.com/taroyamada/procurement-intel-actor) for broader public procurement intelligence. - [SEC EDGAR Material Disclosure Watch Scraper](https://apify.com/taroyamada/sec-edgar-material-disclosure-watch-scraper) for public corporate disclosure watch reports. # Actor input Schema ## `awardSearches` (type: `array`): Search definitions for official public federal award data. ## `maxAwardsPerSearch` (type: `integer`): Max awards per search input. ## `emitBaselineSummary` (type: `boolean`): Emit a baseline summary row on the first run for a new snapshot namespace. ## `emitAwardRows` (type: `boolean`): Emit optional public award observation rows. Off by default; recompete candidates carry the business value. ## `generateReport` (type: `boolean`): Generate a report row when report-worthy rows are emitted. ## `fixturePath` (type: `string`): Optional local fixture path for private canary/testing runs. ## `emitUnchanged` (type: `boolean`): When false, unchanged searches emit no dataset rows and no charges. ## `dryRun` (type: `boolean`): When true, emit sample rows without live calls or billing. ## `initialRunMode` (type: `string`): Deployment canary control used by automation; emit\_backfill allows first-run proof rows, baseline\_only is used for no-change proof. ## `snapshotKey` (type: `string`): Optional state namespace for canary and recurring no-change proof runs. ## Actor input object example ```json { "awardSearches": [ { "id": "sample-it-services", "keywords": [ "cloud", "modernization" ], "recipientNames": [], "naicsCodes": [ "541512" ], "pscCodes": [], "daysUntilEnd": 365, "minimumAwardAmount": 1000000 } ], "maxAwardsPerSearch": 50, "emitBaselineSummary": true, "emitAwardRows": false, "generateReport": true, "fixturePath": "", "emitUnchanged": false, "dryRun": true, "initialRunMode": "emit_backfill", "snapshotKey": "" } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("taroyamada/govcon-recompete-incumbent-watch-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`ðŸ’ū 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 = {} # Run the Actor and wait for it to finish run = client.actor("taroyamada/govcon-recompete-incumbent-watch-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("ðŸ’ū 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 '{}' | apify call taroyamada/govcon-recompete-incumbent-watch-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=taroyamada/govcon-recompete-incumbent-watch-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GovCon Recompete & Incumbent Watch", "description": "Unofficially watch public federal award data for recompete windows and incumbent contractor signals.", "version": "0.1", "x-build-id": "0gLj6BwX7epprWIK9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/taroyamada~govcon-recompete-incumbent-watch-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-taroyamada-govcon-recompete-incumbent-watch-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/taroyamada~govcon-recompete-incumbent-watch-scraper/runs": { "post": { "operationId": "runs-sync-taroyamada-govcon-recompete-incumbent-watch-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/taroyamada~govcon-recompete-incumbent-watch-scraper/run-sync": { "post": { "operationId": "run-sync-taroyamada-govcon-recompete-incumbent-watch-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "awardSearches": { "title": "Award searches", "type": "array", "description": "Search definitions for official public federal award data.", "default": [ { "id": "sample-it-services", "keywords": [ "cloud", "modernization" ], "recipientNames": [], "naicsCodes": [ "541512" ], "pscCodes": [], "daysUntilEnd": 365, "minimumAwardAmount": 1000000 } ] }, "maxAwardsPerSearch": { "title": "Max awards per search", "minimum": 1, "maximum": 500, "type": "integer", "description": "Max awards per search input.", "default": 50 }, "emitBaselineSummary": { "title": "Emit baseline summary", "type": "boolean", "description": "Emit a baseline summary row on the first run for a new snapshot namespace.", "default": true }, "emitAwardRows": { "title": "Emit award observation rows", "type": "boolean", "description": "Emit optional public award observation rows. Off by default; recompete candidates carry the business value.", "default": false }, "generateReport": { "title": "Generate watch report", "type": "boolean", "description": "Generate a report row when report-worthy rows are emitted.", "default": true }, "fixturePath": { "title": "Fixture path", "type": "string", "description": "Optional local fixture path for private canary/testing runs.", "default": "" }, "emitUnchanged": { "title": "Emit unchanged rows", "type": "boolean", "description": "When false, unchanged searches emit no dataset rows and no charges.", "default": false }, "dryRun": { "title": "Dry run", "type": "boolean", "description": "When true, emit sample rows without live calls or billing.", "default": true }, "initialRunMode": { "title": "Initial run mode", "enum": [ "emit_backfill", "baseline_only" ], "type": "string", "description": "Deployment canary control used by automation; emit_backfill allows first-run proof rows, baseline_only is used for no-change proof.", "default": "emit_backfill" }, "snapshotKey": { "title": "Snapshot key", "type": "string", "description": "Optional state namespace for canary and recurring no-change proof runs.", "default": "" } } }, "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 } } } } } } } } } } ```