# Algora Bounty Radar (`sebarb/algora-bounty-radar`) Actor Read-only scout for public Algora/GitHub bounty candidates with safety, funding, and competition filters. - **URL**: https://apify.com/sebarb/algora-bounty-radar.md - **Developed by:** [Seba](https://apify.com/sebarb) (community) - **Categories:** Developer tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 scored bounty 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 ## Algora Bounty Radar Find public open-source bounty leads worth reviewing before you spend implementation time. Algora Bounty Radar is a read-only Apify Actor that searches public GitHub and Algora-style bounty signals, then scores each candidate for funding evidence, payout-route confidence, competition, safety risk, and implementation clarity. The Actor is built for operators, contributors, and agencies that want a smaller, safer review queue instead of a long list of bounty-looking issues. ### What It Does - Searches public GitHub issue data with bounty-focused queries. - Detects public Algora-style funding signals and explicit USD/USDC amounts. - Scores candidates by funding proof, payout route, acceptance criteria, freshness, competition, and safety. - Separates results into `REVIEW`, `MONITOR`, and `REJECT`. - Writes normalized rows to the default Apify dataset. - Writes a compact run summary to the default key-value store output. ### What It Does Not Do - It does not claim bounties. - It does not submit pull requests. - It does not comment on GitHub issues. - It does not guarantee payment, acceptance, or bounty wins. - It does not configure wallets, Stripe, tax, or payout accounts. - It does not use private repos, private mailbox data, cookies, or session data. ### Best Use Cases - Building a shortlist of funded OSS issues for manual review. - Filtering noisy GitHub reward text before assigning engineering time. - Monitoring whether public bounty leads have enough evidence to justify deeper review. - Auditing why a high-dollar issue is still too risky or too vague to pursue. ### Input The main inputs are: - `queries`: GitHub search queries. Keep these narrow and bounty-specific. - `rails`: preferred evidence rails, such as `algora` and `github_text_seed`. - `minAmountUsd`: minimum clear bounty amount. - `maxOpenPrs`: maximum visible open pull requests before a candidate is rejected. - `maxClaimCount`: maximum claim-like comment count before a candidate is rejected. - `maxResults`: maximum rows to return. - `rejectUnsafePatterns`: rejects prompt disclosure, fake engagement, social gates, wallet-funding traps, and unsafe production testing. - `dryRun`: uses provided seed issues or bundled examples and skips live GitHub search. - `seedIssues`: optional issue records for controlled testing. Example: ```json { "queries": [ "algora bounty is:issue is:open", "\"$\" \"bounty\" is:issue is:open" ], "minAmountUsd": 25, "maxOpenPrs": 2, "maxClaimCount": 2, "maxResults": 25, "rejectUnsafePatterns": true, "dryRun": false } ```` ### Output Each dataset row represents one scored candidate. Important fields: - `decision`: `REVIEW`, `MONITOR`, or `REJECT`. - `score`: numeric confidence score. - `rail`: detected evidence rail. - `funding_proof`: funding evidence class, such as `public_bounty_page` or `text_only`. - `payout_route`: expected payout route when it can be inferred from public evidence. - `amount` and `currency`: detected bounty amount. - `repo`, `issue_number`, `title`, `issue_url`, and `bounty_url`. - `risk_flags`: unsafe or low-trust patterns found in the issue text. - `reject_reasons`: concrete reasons the candidate should not receive implementation time. - `next_action`: manual next step recommendation. Example row: ```json { "decision": "REVIEW", "score": 84, "rail": "algora", "funding_proof": "public_bounty_page", "payout_route": "stripe_connect", "amount": 2500, "currency": "USD", "repo": "algora-io/algora", "issue_number": 238, "title": "[UI Bug] Unauthorized 'Edit' and 'Delete' buttons visible on /bounties page", "issue_url": "https://github.com/algora-io/algora/issues/238", "risk_flags": [], "reject_reasons": [], "next_action": "Manual review before any patch-prep packet. This Actor does not claim, comment, or create PRs." } ``` ### Decision Labels `REVIEW` means the candidate has enough public signal for manual due diligence. It is not a patch instruction. `MONITOR` means the candidate has some useful signal but needs better funding, acceptance, or competition evidence. `REJECT` means the Actor found a concrete reason to avoid spending implementation time. ### Data Sources This Actor uses public GitHub issue search data and public issue text. It may infer Algora-style evidence from public references to Algora, bounty pages, or bounty amounts. Inferred payout routes are directional signals, not payout guarantees. ### Operational Notes Use `dryRun: true` for private tests and controlled demos. Use low `maxResults` values for first live runs. Review `REVIEW` rows manually before starting any implementation work. For public Store use, pricing charges for filtering and scoring value, not for bounty outcomes. The intended pay-per-event event is `scored-candidate`, charged once per scored dataset row when the Actor is configured as a paid Apify Actor. # Actor input Schema ## `queries` (type: `array`): Search queries used only for read-only discovery. Keep them narrow and bounty-specific. ## `rails` (type: `array`): Preferred evidence rails. Text-only GitHub matches are scored conservatively. ## `minAmountUsd` (type: `integer`): Ignore candidates with lower clear USD-equivalent amounts. ## `maxOpenPrs` (type: `integer`): Reject candidates with too much visible implementation competition. ## `maxClaimCount` (type: `integer`): Reject candidates where many comments indicate claims or attempts. ## `maxResults` (type: `integer`): Maximum scored candidates to output. ## `rejectUnsafePatterns` (type: `boolean`): Reject prompt disclosure, star gates, fake engagement, vague rewards, stale issues, and closed work. ## `dryRun` (type: `boolean`): When enabled, score only seedIssues or bundled examples and skip live GitHub search. The Actor is read-only regardless of this setting. ## `seedIssues` (type: `array`): Optional local issue records to score before or instead of live search. Useful for private dry runs and local tests. ## Actor input object example ```json { "queries": [ "algora bounty is:issue is:open", "\"$\" \"bounty\" is:issue is:open" ], "rails": [ "algora", "github_text_seed" ], "minAmountUsd": 25, "maxOpenPrs": 2, "maxClaimCount": 2, "maxResults": 25, "rejectUnsafePatterns": true, "dryRun": false, "seedIssues": [] } ``` # Actor output Schema ## `bountyCandidates` (type: `string`): Scored bounty candidate rows written to the default dataset. The overview view highlights decision, score, funding evidence, payout route, amount, issue URL, risk flags, rejection reasons, and next action. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("sebarb/algora-bounty-radar").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("sebarb/algora-bounty-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 sebarb/algora-bounty-radar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sebarb/algora-bounty-radar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Algora Bounty Radar", "description": "Read-only scout for public Algora/GitHub bounty candidates with safety, funding, and competition filters.", "version": "0.1", "x-build-id": "BoZdI6fW4EAmxAvtX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sebarb~algora-bounty-radar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sebarb-algora-bounty-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sebarb~algora-bounty-radar/runs": { "post": { "operationId": "runs-sync-sebarb-algora-bounty-radar", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sebarb~algora-bounty-radar/run-sync": { "post": { "operationId": "run-sync-sebarb-algora-bounty-radar", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "queries", "minAmountUsd", "maxOpenPrs", "maxClaimCount", "maxResults", "rejectUnsafePatterns" ], "properties": { "queries": { "title": "GitHub search queries", "type": "array", "description": "Search queries used only for read-only discovery. Keep them narrow and bounty-specific.", "default": [ "algora bounty is:issue is:open", "\"$\" \"bounty\" is:issue is:open" ], "items": { "type": "string" } }, "rails": { "title": "Allowed rails", "type": "array", "description": "Preferred evidence rails. Text-only GitHub matches are scored conservatively.", "default": [ "algora", "github_text_seed" ], "items": { "type": "string" } }, "minAmountUsd": { "title": "Minimum amount USD", "minimum": 0, "type": "integer", "description": "Ignore candidates with lower clear USD-equivalent amounts.", "default": 25 }, "maxOpenPrs": { "title": "Maximum open PRs", "minimum": 0, "type": "integer", "description": "Reject candidates with too much visible implementation competition.", "default": 2 }, "maxClaimCount": { "title": "Maximum claim count", "minimum": 0, "type": "integer", "description": "Reject candidates where many comments indicate claims or attempts.", "default": 2 }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum scored candidates to output.", "default": 25 }, "rejectUnsafePatterns": { "title": "Reject unsafe patterns", "type": "boolean", "description": "Reject prompt disclosure, star gates, fake engagement, vague rewards, stale issues, and closed work.", "default": true }, "dryRun": { "title": "Dry run", "type": "boolean", "description": "When enabled, score only seedIssues or bundled examples and skip live GitHub search. The Actor is read-only regardless of this setting.", "default": false }, "seedIssues": { "title": "Seed issues", "type": "array", "description": "Optional local issue records to score before or instead of live search. Useful for private dry runs and local tests.", "items": { "type": "object", "properties": { "issue_url": { "type": "string", "title": "GitHub issue URL", "description": "Public GitHub issue URL to score." }, "title": { "type": "string", "title": "Issue title", "description": "Issue title or short candidate headline." }, "body": { "type": "string", "title": "Issue body or notes", "description": "Issue body, labels, or notes used for read-only scoring." }, "rail": { "type": "string", "title": "Evidence rail", "description": "Optional evidence rail such as algora, github_text_seed, or auto." }, "funding_proof": { "type": "string", "title": "Funding proof", "description": "Optional funding proof classification such as public_bounty_page, text_only, or auto." }, "payout_route": { "type": "string", "title": "Payout route", "description": "Optional expected payout route such as stripe_connect or unknown." }, "issue_state": { "type": "string", "title": "Issue state", "description": "Issue state, normally open for actionable candidates." }, "open_pr_count": { "type": "integer", "title": "Open PR count", "description": "Visible open pull request count for competition scoring." }, "claim_count": { "type": "integer", "title": "Claim count", "description": "Visible claim-like comment count for competition scoring." } } }, "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 } } } } } } } } } } ```