apify-actorization

# Apify Actorization Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output. ## Quick Start 1. Run `apify init` in project root 2. Wrap code with SDK lifecycle (see language-specific section below) 3. Configure `.actor/input_schema.json` 4. Test with `apify run --input '{"key": "value"}'` 5. Deploy with `apify push` ## When to Use This Skill - Converting an existing project to run on Apify platform - Adding Apify SDK integration to a project - Wrapping a CLI tool or script as an Actor - Migrating a Crawlee project to Apify ## Prerequisites Verify `apify` CLI is installed: ```bash apify --help ``` If not installed: ```bash brew install apify-cli # Or: npm install -g apify-cli # Or install from an official release package that your OS package manager verifies ``` Verify CLI is logged in: ```bash apify info # Should return your username ``` If not logged in, check if `APIFY_TOKEN` environment variable is defined. If not, ask the user to generate one at https://console.apify.com/settings/integrations, add it to their shell or secret manager without putting the literal token in command history, then run: ```bash apify login ``` ## Actorization Checklist Copy this checklist to track progress: - [ ] Step 1: Analyze project (language, entry point, inputs, outputs) - [ ] Step 2: Run `apify init` to create Actor structure - [ ] Step 3: Apply language-specific SDK integration - [ ] Step 4: Configure `.actor/input_schema.json` - [ ] Step 5: Configure `.actor/output_schema.json` (if applicable) - [ ] Step 6: Update `.actor/actor.json` metadata - [ ] Step 7: Test locally with `apify run` - [ ] Step 8: Deploy with `apify push` ## Step 1: Analyze the Project Before making changes, understand the project: 1. **Identify the language** - JavaScript/TypeScript, Python, or other 2. **Find the entry point** - The main file that starts execution 3. **Identify inputs** - Command-line arguments, environment variables, config files 4. **Identify outputs** - Files, console output, API responses 5. **Check for state** - Does it need to persist data between runs? ## Step 2: Initialize Actor Structure Run in the project root: ```bash apify init ``` This creates: - `.actor/actor.json` - Actor configuration and metadata - `.actor/input_schema.json` - Input definition for the Apify Console - `Dockerfile` (if not present) - Container image definition ## Step 3: Apply Language-Specific Changes Choose based on your project's language: - **JavaScript/TypeScript**: See [js-ts-actorization.md](references/js-ts-actorization.md) - **Python**: See [python-actorization.md](references/python-actorization.md) - **Other Languages (CLI-based)**: See [cli-actorization.md](references/cli-actorization.md) ### Quick Reference | Language | Install | Wrap Code | |----------|---------|-----------| | JS/TS | `npm install apify` | `await Actor.init()` ... `await Actor.exit()` | | Python | `pip install apify` | `async with Actor:` | | Other | Use CLI in wrapper script | `apify actor:get-input` / `apify actor:push-data` | ## Steps 4-6: Configure Schemas See [schemas-and-output.md](references/schemas-and-output.md) for detailed configuration of: - Input schema (`.actor/input_schema.json`) - Output schema (`.actor/output_schema.json`) - Actor configuration (`.actor/actor.json`) - State management (request queues, key-value stores) Validate schemas against `@apify/json_schemas` npm package. ## Step 7: Test Locally Run the actor with inline input (for JS/TS and Python actors): ```bash apify run --input '{"startUrl": "https://example.com", "maxItems": 10}' ``` Or use an input file: ```bash apify run --input-file ./test-input.json ``` **Important:** Always use `apify run`, not `npm start` or `python main.py`. The CLI sets up the proper environment and storage. ## Step 8: Deploy ```bash apify push ``` This uploads and builds your actor on the Apify platform. ## Monetization (Optional) After deploying, you can monetize your actor in the Apify Store. The recommended model is **Pay Per Event (PPE)**: - Per result/item scraped - Per page processed - Per API call made Configure PPE in the Apify Console under Actor > Monetization. Charge for events in your code with `await Actor.charge('result')`. Other options: **Rental** (monthly subscription) or **Free** (open source). ## Pre-Deployment Checklist - [ ] `.actor/actor.json` exists with correct name and description - [ ] `.actor/actor.json` validates against `@apify/json_schemas` (`actor.schema.json`) - [ ] `.actor/input_schema.json` defines all required inputs - [ ] `.actor/input_schema.json` validates against `@apify/json_schemas` (`input.schema.json`) - [ ] `.actor/output_schema.json` defines output structure (if applicable) - [ ] `.actor/output_schema.json` validates against `@apify/json_schemas` (`output.schema.json`) - [ ] `Dockerfile` is present and builds successfully - [ ] `Actor.init()` / `Actor.exit()` wraps main code (JS/TS) - [ ] `async with Actor:` wraps main code (Python) - [ ] Inputs are read via `Actor.getInput()` / `Actor.get_input()` - [ ] Outputs use `Actor.pushData()` or key-value store - [ ] `apify run` executes successfully with test input - [ ] `generatedBy` is set in actor.json meta section ## Apify MCP Tools If MCP server is configured, use these tools for documentation: - `search-apify-docs` - Search documentation - `fetch-apify-docs` - Get full doc pages Otherwise, the MCP Server url: `https://mcp.apify.com/?tools=docs`. ## Resources - [Actorization Academy](https://docs.apify.com/academy/actorization) - Comprehensive guide - [Apify SDK for JavaScript](https://docs.apify.com/sdk/js) - Full SDK reference - [Apify SDK for Python](https://docs.apify.com/sdk/python) - Full SDK reference - [Apify CLI Reference](https://docs.apify.com/cli) - CLI commands - [Actor Specification](https://raw.githubusercontent.com/apify/actor-whitepaper/refs/heads/master/README.md) - Complete specification ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.