# GitHub Bounty Scout (`fragrant_invite/github-bounty-scout`) Actor Find actionable GitHub bounty and paid issue leads before they get buried in noisy search results. - **URL**: https://apify.com/fragrant\_invite/github-bounty-scout.md - **Developed by:** [玉成 孙](https://apify.com/fragrant_invite) (community) - **Categories:** Developer tools - **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 Bounty Scout Find public GitHub bounty issues that look actionable, then filter out noisy leads such as pull requests, archived repositories, already-submitted claims, and crowded attempt threads. ### Why this exists Open bounty hunting is noisy. Many issues mention `$50` or `bounty`, but are already claimed, archived, paid out, or not actually a paid task. This Actor turns raw GitHub search queries into a ranked lead list with reasons and rejection flags. ### Input - `queries`: GitHub issue search queries. - `maxResultsPerQuery`: number of search results to inspect for each query. - `minimumScore`: only emit leads at or above this score. - `githubToken`: optional token for higher GitHub API limits. ### Output Each dataset item includes: - repository, issue number, title, URL - score from 0 to 100 - positive reasons - risk flags - comment count and timestamps ### Suggested monetization For Apify Store, start with pay-per-event pricing: - Event: `apify-default-dataset-item` - Starter price: `$0.05` to `$0.20` per qualified lead - Keep a low free/demo run limit so users can inspect output quality This is data engineering only. It does not guarantee bounty acceptance or payment. ### Local development ```bash npm install npm test GITHUB_TOKEN=github_pat_or_classic_token npm run demo ```` Without a token, GitHub's public API rate limit can be exhausted quickly. The Actor input schema includes `githubToken` as a secret field for cloud runs. # Actor input Schema ## `queries` (type: `array`): GitHub issue search queries. Keep them focused on public bounty signals. ## `maxResultsPerQuery` (type: `integer`): Maximum GitHub search results to inspect for each query. ## `minimumScore` (type: `integer`): Only output leads with this score or higher. ## `githubToken` (type: `string`): Optional GitHub token for higher API limits. Leave blank for public unauthenticated runs. ## Actor input object example ```json { "queries": [ "\"/bounty $50\" state:open comments:0..5", "\"💎 $50 bounty\" \"Steps to solve\" state:open comments:0..8" ], "maxResultsPerQuery": 10, "minimumScore": 40 } ``` # 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 = { "queries": [ "\"/bounty $50\" state:open comments:0..5", "\"💎 $50 bounty\" \"Steps to solve\" state:open comments:0..8" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fragrant_invite/github-bounty-scout").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "queries": [ "\"/bounty $50\" state:open comments:0..5", "\"💎 $50 bounty\" \"Steps to solve\" state:open comments:0..8", ] } # Run the Actor and wait for it to finish run = client.actor("fragrant_invite/github-bounty-scout").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 '{ "queries": [ "\\"/bounty $50\\" state:open comments:0..5", "\\"💎 $50 bounty\\" \\"Steps to solve\\" state:open comments:0..8" ] }' | apify call fragrant_invite/github-bounty-scout --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fragrant_invite/github-bounty-scout", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GitHub Bounty Scout", "description": "Find actionable GitHub bounty and paid issue leads before they get buried in noisy search results.", "version": "0.1", "x-build-id": "IRtdTpL5ShlSyxgjw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fragrant_invite~github-bounty-scout/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fragrant_invite-github-bounty-scout", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/fragrant_invite~github-bounty-scout/runs": { "post": { "operationId": "runs-sync-fragrant_invite-github-bounty-scout", "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/fragrant_invite~github-bounty-scout/run-sync": { "post": { "operationId": "run-sync-fragrant_invite-github-bounty-scout", "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": [ "queries" ], "properties": { "queries": { "title": "GitHub search queries", "type": "array", "description": "GitHub issue search queries. Keep them focused on public bounty signals.", "default": [ "\"/bounty $50\" state:open comments:0..5", "\"💎 $50 bounty\" \"Steps to solve\" state:open comments:0..8", "\"paid\" \"good first issue\" state:open comments:0..8 updated:>2026-01-01" ], "items": { "type": "string" } }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 1, "maximum": 30, "type": "integer", "description": "Maximum GitHub search results to inspect for each query.", "default": 10 }, "minimumScore": { "title": "Minimum score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only output leads with this score or higher.", "default": 40 }, "githubToken": { "title": "GitHub token", "type": "string", "description": "Optional GitHub token for higher API limits. Leave blank for public unauthenticated runs." } } }, "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 } } } } } } } } } } ```