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.
Best use case
apify-actorization is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "apify-actorization" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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?
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.
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.
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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
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 specificationRelated Skills
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