apify-actorization

apify/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/apify/agent-skills --skill apify-actorization
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summary

Convert existing projects into serverless Apify Actors with language-specific SDK integration.

  • Supports JavaScript/TypeScript (with Actor.init() / Actor.exit() ), Python (async context manager), and any language via CLI wrapper
  • Provides structured workflow: apify init to scaffold, apply SDK wrapping, configure input/output schemas, test locally with apify run , then deploy with apify push
  • Includes input and output schema validation, Docker containerization, and optional pay-per-event
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

  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, 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, check if the APIFY_TOKEN environment variable is defined (if not, ask the user to generate one at https://console.apify.com/settings/integrations and then define APIFY_TOKEN with it).

Then authenticate using one of these methods:

# Option 1 (preferred): The CLI automatically reads APIFY_TOKEN from the environment.
# Just ensure the env var is exported and run any apify command — no explicit login needed.

# Option 2: Interactive login (prompts for token without exposing it in shell history)
apify login

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 environment variables or interactive login 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 the 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:

  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:

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:

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 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).

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

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

how to use apify-actorization

How to use apify-actorization on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add apify-actorization
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/apify/agent-skills --skill apify-actorization

The skills CLI fetches apify-actorization from GitHub repository apify/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/apify-actorization

Reload or restart Cursor to activate apify-actorization. Access the skill through slash commands (e.g., /apify-actorization) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.574 reviews
  • Kiara Lopez· Dec 28, 2024

    apify-actorization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakura Abebe· Dec 24, 2024

    We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Fatima Menon· Dec 20, 2024

    Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.

  • Fatima Jain· Dec 16, 2024

    Useful defaults in apify-actorization — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Dec 4, 2024

    Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.

  • Piyush G· Nov 23, 2024

    We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Zara Huang· Nov 23, 2024

    apify-actorization reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Fatima Khanna· Nov 19, 2024

    Useful defaults in apify-actorization — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Tariq Thomas· Nov 15, 2024

    Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.

  • Kiara Gonzalez· Nov 11, 2024

    We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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