# Actor README Generator Agent (`jkuzz/actor-readme-generator-agent`) Actor Generates a README for an Apify Actor using an AI Agent. You only need to provide the actor id to generate a readable and SEO optimized README for the Actor. The generation process utilizes Apify's Official README Guide. This Agent is open source (link in readme). - **URL**: https://apify.com/jkuzz/actor-readme-generator-agent.md - **Developed by:** [Jan KuΕΎelΓ­k](https://apify.com/jkuzz) (community) - **Categories:** AI, Open source, Agents - **Stats:** 22 total users, 0 monthly users, 100.0% runs succeeded, 5 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. 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 [![Actor README Generator Agent](https://apify.com/actor-badge?actor=jkuzz/actor-readme-generator-agent)](https://apify.com/jkuzz/actor-readme-generator-agent) ## Actor README Generator Agent This is an open source Apify Actor agent. You can find the source code at https://github.com/Jkuzz/actor-readme-generator ### Introduction and Features The Actor README Generator is designed to automate the generation of structured README files for Apify Actors. It simplifies the process of creating well-organized and SEO-optimized documentation for your actor, ensuring that all critical information is included. #### Key Functionalities: - Generates README files based on Actor input schema. - Provides pricing information based on usage scenarios. - Supports various input models, including OpenAI's gpt-4o and gpt-4o-mini. - Enables debugging for easier troubleshooting. #### Pricing Model: The pricing model for the AI Actor README Generator is based on a pay-per-event system. Here are the key pricing details: | Event Title | Description | Price (USD) | | ------------------------------------------- | ----------------------------------------------------------- | ----------- | | Actor start per 1 GB | Flat fee for starting an Actor run for each 1 GB of memory. | $0.005 | | Price per 100 OpenAI tokens for gpt-4o | Flat fee for each 100 gpt-4o tokens used. | $0.001 | | Price per 100 OpenAI tokens for gpt-4o-mini | Flat fee for each 100 gpt-4o-mini tokens used. | $0.00006 | ### How to Use Actor README Generator #### Step-by-step Guide: 1. Input the Actor ID and select your desired OpenAI model (gpt-4o or gpt-4o-mini). 2. Optionally enable debug mode for detailed output during execution. 3. Run the Actor to generate a structured README file. #### Example of Input ```json { "actorId": "JOHAgNsmklA9ZBl7Y", "modelName": "gpt-4o-mini", "debug": false } ```` #### Expected Output The output will be a structured README in Markdown format, including details such as: - Actor functionalities - Pricing model - Input and output examples - FAQs and support information ### FAQ #### What is the pricing for using this Actor? This Actor follows a pay-per-event pricing model, where fees are incurred based on memory usage and OpenAI token consumption. #### Can I use this Actor without coding experience? Yes, the Actor is designed to be user-friendly and requires no coding knowledge to operate effectively. #### How can I seek support if I encounter issues? For support, you can refer to the [official documentation](https://apify.com) or reach out through the support channels provided on the Apify platform. # Actor input Schema ## `actorId` (type: `string`): Apify id of the actor to generate the README for. All necessary information will be fetched from the Actor automatically. ## `modelName` (type: `string`): The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini. ## `debug` (type: `boolean`): If enabled, the Actor will run in debug mode and produce more output. ## Actor input object example ```json { "actorId": "JOHAgNsmklA9ZBl7Y", "modelName": "gpt-4o-mini", "debug": false } ``` # 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 = { "actorId": "JOHAgNsmklA9ZBl7Y", "modelName": "gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("jkuzz/actor-readme-generator-agent").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "actorId": "JOHAgNsmklA9ZBl7Y", "modelName": "gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("jkuzz/actor-readme-generator-agent").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "actorId": "JOHAgNsmklA9ZBl7Y", "modelName": "gpt-4o-mini" }' | apify call jkuzz/actor-readme-generator-agent --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jkuzz/actor-readme-generator-agent", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Actor README Generator Agent", "description": "Generates a README for an Apify Actor using an AI Agent. You only need to provide the actor id to generate a readable and SEO optimized README for the Actor. The generation process utilizes Apify's Official README Guide. This Agent is open source (link in readme).", "version": "1.0", "x-build-id": "JAuydEmjpq0qdvy5z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jkuzz~actor-readme-generator-agent/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jkuzz-actor-readme-generator-agent", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jkuzz~actor-readme-generator-agent/runs": { "post": { "operationId": "runs-sync-jkuzz-actor-readme-generator-agent", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jkuzz~actor-readme-generator-agent/run-sync": { "post": { "operationId": "run-sync-jkuzz-actor-readme-generator-agent", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "actorId" ], "properties": { "actorId": { "title": "Actor ID", "type": "string", "description": "Apify id of the actor to generate the README for. All necessary information will be fetched from the Actor automatically." }, "modelName": { "title": "OpenAI model", "enum": [ "gpt-4o", "gpt-4o-mini" ], "type": "string", "description": "The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini.", "default": "gpt-4o-mini" }, "debug": { "title": "Debug", "type": "boolean", "description": "If enabled, the Actor will run in debug mode and produce more output.", "default": false } } }, "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 } } } } } } } } } } ```