apify-actorization

SKILL.md

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 in project root

Wrap code with SDK lifecycle (see language-specific section below)

Configure .actor/input_schema.json

Test with apify run --input '{"key": "value"}'

Deploy with apify push

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 (opens browser):

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.

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: Write README.md for Apify Store listing

 Step 8: Test locally with apify run

 Step 9: Deploy with apify push

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:

apify init

This creates:

.actor/actor.json - Actor configuration and metadata

.actor/input_schema.json - Input definition for 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

Python: See python-actorization.md

Other Languages (CLI-based): See 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 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: 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:

Instagram Scraper

Google Maps Scraper

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}'

Or use an input file:

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 9: Deploy

apify push

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('result').

Other options: Rental (monthly subscription) or Free (open source).

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

 .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

 README.md exists with proper structure (intro, features, data table, tutorial, pricing, input/output examples)

 generatedBy is set in actor.json meta section

MCP tools

Apify MCP

If the Apify 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.

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

Actorization Academy - Comprehensive guide

Apify SDK for JavaScript - Full SDK reference

Apify SDK for Python - Full SDK reference

Apify CLI Reference - CLI commands

Actor Specification - Complete specification