# Continue.dev Hub Catalog Scraper (`jungle_synthesizer/continuedev-hub-assistant-catalog-scraper`) Actor Scrapes the Continue.dev hub catalog — AI coding assistant rules, prompts, models, MCP server blocks, and docs packages. Extracts metadata, config YAML, raw markdown, star counts, and author details. - **URL**: https://apify.com/jungle\_synthesizer/continuedev-hub-assistant-catalog-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 40.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Continue.dev Hub Catalog Scraper Scrapes the complete [Continue.dev](https://continue.dev) hub catalog — the canonical marketplace for AI coding assistant configurations. Extracts rules, prompts, models, documentation packages, and MCP server blocks with full config YAML, star counts, and author details. ### What it scrapes The Continue.dev hub (`https://continue.dev/hub`) is an open marketplace where teams publish their AI coding assistant configurations. This actor collects all public packages across these categories: | Type | Examples | |------|---------| | `rules` | Coding standards, project conventions (e.g. Angular Rules, Next.js Rules) | | `prompts` | Reusable prompts for common workflows | | `models` | Model configuration blocks (OpenAI GPT-4o, Claude, Gemini, Llama, etc.) | | `docs` | Framework documentation packages | | `blocks` | Composite configuration blocks | | `mcp-servers` | MCP server integration configs | ### Use cases - **Enterprise config auditing** — inventory all publicly-shared Continue.dev configs to benchmark your team's setup against community standards - **AI dev-tool intelligence** — track adoption of models, coding standards, and MCP integrations across the developer community - **Config discovery** — find the most-starred rules/prompts for a specific framework before building your own - **Research** — analyze trends in AI coding assistant configuration patterns ### Output fields | Field | Type | Description | |-------|------|-------------| | `item_id` | string | Unique ID in `author/slug` format (e.g. `starter/angular-rules`) | | `item_type` | string | Package type: `rules`, `prompts`, `docs`, `models`, `blocks`, `mcp-servers` | | `author` | string | Owner/author slug | | `name` | string | Display name of the package | | `tagline` | string | Short description | | `is_official` | boolean | Whether this is a verified/official package | | `star_count` | integer | Number of stars | | `use_count` | integer | Number of installs/uses | | `raw_markdown` | string | Full YAML/Markdown content of the package config | | `profile_url` | string | Direct URL to the package page | | `scraped_at` | string | ISO-8601 scrape timestamp | ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | 50 | Maximum number of items to scrape | | `itemTypes` | array | all types | Filter to specific types: `rules`, `prompts`, `docs`, `models`, `blocks`, `mcp-servers` | ### Sample output ```json { "item_id": "starter/angular-rules", "item_type": "rules", "author": "starter", "name": "Continue * starter/angular-rules", "tagline": null, "is_official": true, "star_count": 9, "use_count": 0, "raw_markdown": "---\nname: Angular Rules\n---\n\n- You are an Angular developer\n- Use Angular CLI for project scaffolding\n...", "profile_url": "https://www.continue.dev/starter/angular-rules", "scraped_at": "2026-06-12T14:30:00.000Z" } ```` ### Technical notes - No authentication required — all hub packages are publicly accessible - No proxy required — datacenter IPs work fine - Typical full run: ~100-120 items across all types, completes in under 2 minutes - Data is extracted from Next.js RSC streaming payloads embedded in the HTML # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of items to scrape. Leave blank to scrape all items. ## `itemTypes` (type: `array`): Types of hub items to scrape. Leave empty to scrape all types. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "itemTypes": [ "rules", "prompts", "docs", "models", "blocks", "mcp-servers" ] } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "itemTypes": [ "rules", "prompts", "docs", "models", "blocks", "mcp-servers" ] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/continuedev-hub-assistant-catalog-scraper").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "itemTypes": [ "rules", "prompts", "docs", "models", "blocks", "mcp-servers", ], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/continuedev-hub-assistant-catalog-scraper").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "itemTypes": [ "rules", "prompts", "docs", "models", "blocks", "mcp-servers" ] }' | apify call jungle_synthesizer/continuedev-hub-assistant-catalog-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/continuedev-hub-assistant-catalog-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Continue.dev Hub Catalog Scraper", "description": "Scrapes the Continue.dev hub catalog — AI coding assistant rules, prompts, models, MCP server blocks, and docs packages. Extracts metadata, config YAML, raw markdown, star counts, and author details.", "version": "0.1", "x-build-id": "rCnyyyzWG4eD3eBpB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~continuedev-hub-assistant-catalog-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-continuedev-hub-assistant-catalog-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/jungle_synthesizer~continuedev-hub-assistant-catalog-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-continuedev-hub-assistant-catalog-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/jungle_synthesizer~continuedev-hub-assistant-catalog-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-continuedev-hub-assistant-catalog-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": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of items to scrape. Leave blank to scrape all items." }, "itemTypes": { "title": "Item Types", "type": "array", "description": "Types of hub items to scrape. Leave empty to scrape all types.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```