apify-actorization
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Us...
Best use case
apify-actorization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Us...
Teams using apify-actorization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/apify-actorization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How apify-actorization Compares
| Feature / Agent | apify-actorization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Us...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
curl -fsSL https://apify.com/install-cli.sh | bash
# Or (Mac): brew install apify-cli
# Or (Windows): irm https://apify.com/install-cli.ps1 | iex
# Or: npm install -g apify-cli
```
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, then:
```bash
apify login -t $APIFY_TOKEN
```
## 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 specificationRelated Skills
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