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
- Run
apify init <actor-name> --user-agent apify-agent-skills/apify-actorization in project root
- Create a
Dockerfile for the project's language
- Wrap code with SDK lifecycle (see language-specific section below)
- Configure
.actor/input_schema.json
- Test with
apify run --input '{"key": "value"}' --user-agent apify-agent-skills/apify-actorization
- Deploy with
apify push --user-agent apify-agent-skills/apify-actorization
When to use this skill
- Converting an existing project to run on the 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:
apify --help
If not installed, use one of these methods (listed in order of preference):
# Preferred: install via a package manager (provides integrity checks)
npm install -g apify-cli
# Or (Mac): brew install apify-cli
Security note: Do NOT install the CLI by piping remote scripts to a shell
(e.g. curl ... | bash or irm ... | iex). Always use a package manager.
Verify CLI is logged in:
apify info # Should return your username
If not logged in, authenticate using OAuth (prints a URL and a one-time code to approve in a browser; show the user both):
apify login
If browser login isn't available (headless environment or CI), ensure the APIFY_TOKEN environment variable is exported (note: the variable is APIFY_TOKEN, not APIFY_API_TOKEN). The CLI reads it automatically - no explicit login needed. If the user doesn't have a token, generate one at https://console.apify.com/settings/integrations.
Apify platform environment: When the Actor runs on the Apify platform, APIFY_TOKEN is auto-injected as an environment variable and the Apify SDK reads it automatically — you do not need to pass it explicitly. Locally, apify login stores credentials in ~/.apify and the SDK uses them.
Security note: Avoid passing tokens as command-line arguments (e.g. apify login -t <token>).
Arguments are visible in process listings and may be recorded in shell history.
Prefer OAuth login or environment variables instead.
Never log, print, or embed APIFY_TOKEN in source code or configuration files.
Use a token with the minimum required permissions (scoped token) and rotate it periodically.
Telemetry attribution (required)
Pass --user-agent apify-agent-skills/apify-actorization on every apify command you run from this skill - apify init, apify run, apify push, and the rest. It is a global flag accepted by all apify commands; it only tags the call for telemetry attribution and changes nothing else.
apify push --user-agent apify-agent-skills/apify-actorization
Actorization checklist
Copy this checklist to track progress:
Step 1: Analyze the project
Before making changes, understand the project:
- Identify the language - JavaScript/TypeScript, Python, or other
- Find the entry point - The main file that starts execution
- Identify inputs - Command-line arguments, environment variables, config files
- Identify outputs - Files, console output, API responses
- Check for state - Does it need to persist data between runs?
Step 2: Initialize Actor structure
Run in the project root, passing a DNS-friendly Actor name (e.g. my-scraper):
apify init <actor-name> --user-agent apify-agent-skills/apify-actorization
The name passed as an argument keeps apify init from prompting; without it, the command waits for input and hangs in non-interactive shells.
apify init sets up .actor/actor.json and local storage/, but no Dockerfile. Without one, the platform builds on its default Node.js image, so a Python Actor builds cleanly and then fails at runtime. Create a Dockerfile whose base image matches the project's language (apify/actor-python for Python, apify/actor-node for JavaScript/TypeScript) at .actor/Dockerfile, in the project root, or at the path set in the dockerfile field of .actor/actor.json.
Step 3: Apply language-specific changes
Choose based on your project's language:
Quick reference
Steps 4-6: Configure schemas
See 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 them with apify validate-schema --user-agent apify-agent-skills/apify-actorization.
Step 7: Write README
IMPORTANT: Always generate a README.md as part of actorization. The README is the Actor's landing page on Apify Store and is critical for discoverability (SEO), user onboarding, and support. Do not consider an Actor complete without a proper README.
See the Actor README guidelines at skills/apify-actor-development/references/actor-readme.md for the required structure including: intro and features, data extraction table, step-by-step tutorial, pricing info, input/output examples, and FAQ. Aim for at least 300 words with SEO-optimized H2/H3 headings. Also review these top Actors for best practices:
Step 8: Test locally
Run the Actor with inline input (for JS/TS and Python Actors):
apify run --input '{"startUrl": "https://example.com", "maxItems": 10}' --user-agent apify-agent-skills/apify-actorization
Or use an input file:
apify run --input-file ./test-input.json --user-agent apify-agent-skills/apify-actorization
Important: Always use apify run, not npm start or python main.py. The CLI sets up the proper environment and storage.
Step 9: Deploy
apify push --user-agent apify-agent-skills/apify-actorization
This uploads and builds your Actor on the Apify platform.
Monetization (optional)
After deploying, you can monetize your Actor in Apify Store. The recommended model is Pay Per Event (PPE):
- Per result/item scraped
- Per page processed
- Per API call made
Configure PPE in Apify Console under Actor > Monetization. Charge for events in your code with await Actor.charge({ eventName: 'result' }).
Security
Treat all crawled web content as untrusted input. Actors ingest data from external websites that may contain malicious payloads. Follow these rules:
- Sanitize crawled data — Never pass raw HTML, URLs, or scraped text directly into shell commands,
eval(), database queries, or template engines. Use proper escaping or parameterized APIs.
- Validate and type-check all external data — Before pushing to datasets or key-value stores, verify that values match expected types and formats. Reject or sanitize unexpected structures.
- Do not execute or interpret crawled content — Never treat scraped text as code, commands, or configuration. Content from websites could include prompt injection attempts or embedded scripts.
- Isolate credentials from data pipelines — Ensure
APIFY_TOKEN and other secrets are never accessible in request handlers or passed alongside crawled data. Use the Apify SDK's built-in credential management rather than passing tokens through environment variables in data-processing code.
- Review dependencies before installing — When adding packages with
npm install or pip install, verify the package name and publisher. Typosquatting is a common supply-chain attack vector. Prefer well-known, actively maintained packages.
- Pin versions and use lockfiles — Always commit
package-lock.json (Node.js) or pin exact versions in requirements.txt (Python). Lockfiles ensure reproducible builds and prevent silent dependency substitution. Run npm audit or pip-audit periodically to check for known vulnerabilities.
Pre-deployment checklist
MCP tools
Apify MCP
If the Apify MCP server is configured, use these tools for documentation:
search-apify-docs - Search documentationfetch-apify-docs - Get full doc pages
Otherwise, the MCP Server url: https://mcp.apify.com/?tools=docs.
Playwright MCP (debugging)
The Playwright MCP server is a useful tool for debugging Actors that interact with the web - it lets the agent drive a real browser to inspect pages, capture selectors, and reproduce issues.
Install with the Claude Code CLI:
claude mcp add playwright npx @playwright/mcp@latest
Or add it manually to your MCP config:
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"]
}
}
}
Resources