# 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# 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 <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/eze0ABOWHBwILXNjX/builds/IRtdTpL5ShlSyxgjw/openapi.json