# GitHub Opportunity Radar (`toronto_777/github-opportunity-radar`) Actor Find and rank public GitHub issues that are practical contribution opportunities using keywords, language, stars, and activity filters. - **URL**: https://apify.com/toronto\_777/github-opportunity-radar.md - **Developed by:** [Steven Feng](https://apify.com/toronto_777) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## GitHub Opportunity Radar GitHub Opportunity Radar searches public GitHub issues and ranks practical contribution opportunities for independent developers. It is intended for discovery and prioritization, not for acting on GitHub content. ### What It Does - Searches public GitHub issues from user-provided keywords. - Applies lightweight filters such as language, repository stars, and excluded repositories. - Scores opportunities based on issue and repository signals. - Returns ranked records that can be reviewed before a contributor chooses what to work on. ### Input Fields - `keywords`: issue search terms. - `languages`: optional repository languages. - `minStars`: minimum repository stars. - `maxResults`: maximum records. - `excludeRepos`: public repositories to skip, using `owner/repo` full names. - `includeClosedRecently`: when true, includes recently updated closed issues in search results. - `githubToken`: optional secret for higher API limits. ### Output Fields Each dataset item includes: - `status`: `ok`, `partial`, or `failed`. - `errors`: non-fatal limitations or per-record errors. - `evidence`: source URLs used for the record. - `observedAt`: ISO timestamp for the run. - `keyword`: matched input keyword. - `searchQuery`: GitHub search query used to find the issue. - `repository`: `fullName`, `url`, `stars`, `language`, and `pushedAt`. - `issue`: `id`, `number`, `title`, `url`, `state`, `labels`, `createdAt`, `updatedAt`, `comments`, and `assignees`. - `opportunityScore`: numeric opportunity ranking. - `riskFlags`: risk labels that explain possible drawbacks. - `suggestedAction`: manual follow-up recommendation. ### Limits And Safety Boundaries This Actor uses public GitHub search/API data only. It does not claim issues, comment on GitHub, open pull requests, inspect private repositories, or make changes to repositories. Linked pull request timeline analysis is not included in the MVP. Excluded repositories are applied after lightweight candidate collection because GitHub issue search returns repository API URLs, not guaranteed `owner/repo` names. # Actor input Schema ## `keywords` (type: `array`): Issue search terms to look for, such as labels or bounty-related words. ## `languages` (type: `array`): Optional GitHub language qualifiers used to narrow the issue search. ## `minStars` (type: `integer`): Minimum repository star count required after repository enrichment. ## `maxResults` (type: `integer`): Maximum number of ranked opportunities to return. ## `excludeRepos` (type: `array`): Repository full names to exclude, for example owner/name. ## `includeClosedRecently` (type: `boolean`): Whether to include recently closed issue signals in the opportunity analysis. ## `githubToken` (type: `string`): Optional GitHub token for higher API rate limits. ## Actor input object example ```json { "keywords": [ "good first issue", "bounty" ], "languages": [ "TypeScript" ], "minStars": 100, "maxResults": 25, "excludeRepos": [], "includeClosedRecently": false } ```` # Actor output Schema ## `results` (type: `string`): Dataset items with ranked public GitHub issues, repository metadata, evidence URLs, scores, risk flags, and suggested actions. ## `summary` (type: `string`): Key-value store OUTPUT record containing totalRecords and the returned records for this run. # 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("toronto_777/github-opportunity-radar").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 = {} # Run the Actor and wait for it to finish run = client.actor("toronto_777/github-opportunity-radar").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 '{}' | apify call toronto_777/github-opportunity-radar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=toronto_777/github-opportunity-radar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GitHub Opportunity Radar", "description": "Find and rank public GitHub issues that are practical contribution opportunities using keywords, language, stars, and activity filters.", "version": "0.1", "x-build-id": "xHeaZh0DKjUb3BWCw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/toronto_777~github-opportunity-radar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-toronto_777-github-opportunity-radar", "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/toronto_777~github-opportunity-radar/runs": { "post": { "operationId": "runs-sync-toronto_777-github-opportunity-radar", "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/toronto_777~github-opportunity-radar/run-sync": { "post": { "operationId": "run-sync-toronto_777-github-opportunity-radar", "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": { "keywords": { "title": "Keywords", "minItems": 1, "maxItems": 10, "type": "array", "description": "Issue search terms to look for, such as labels or bounty-related words.", "default": [ "good first issue", "bounty" ], "items": { "type": "string" } }, "languages": { "title": "Languages", "maxItems": 10, "type": "array", "description": "Optional GitHub language qualifiers used to narrow the issue search.", "default": [ "TypeScript" ], "items": { "type": "string" } }, "minStars": { "title": "Minimum stars", "type": "integer", "description": "Minimum repository star count required after repository enrichment.", "default": 100 }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of ranked opportunities to return.", "default": 25 }, "excludeRepos": { "title": "Excluded repositories", "maxItems": 100, "type": "array", "description": "Repository full names to exclude, for example owner/name.", "default": [], "items": { "type": "string" } }, "includeClosedRecently": { "title": "Include closed issues", "type": "boolean", "description": "Whether to include recently closed issue signals in the opportunity analysis.", "default": false }, "githubToken": { "title": "GitHub token", "type": "string", "description": "Optional GitHub token for higher API rate limits." } } }, "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 } } } } } } } } } } ```