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:

apify --help

If not installed:

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:

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:

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:

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
• Python: See python-actorization.md
• Other Languages (CLI-based): See cli-actorization.md

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 schemas against @apify/json_schemas npm package.

Step 7: 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 8: Deploy

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 - 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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Apify Actorization"

Storing Results

After completing work, store backend/API decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags apify-actorization backend

Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>

Agent Team: Code Review

After implementation, dispatch code_review_team for two-stage review (spec compliance + code quality) before merging.