Convert existing projects into serverless Apify Actors with language-specific SDK integration.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionapify-actorizationExecute the skills CLI command in your project's root directory to begin installation:
Fetches apify-actorization from apify/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate apify-actorization. Access via /apify-actorization in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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.
apify init in project root.actor/input_schema.jsonapify run --input '{"key": "value"}'apify pushVerify 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 … | bashorirm … | 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 embedAPIFY_TOKENin source code or configuration files. Use a token with the minimum required permissions (scoped token) and rotate it periodically.
Copy this checklist to track progress:
apify init to create Actor structure.actor/input_schema.json.actor/output_schema.json (if applicable).actor/actor.json metadataapify runapify pushBefore making changes, understand the project:
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 ConsoleDockerfile (if not present) - Container image definitionChoose based on your project's language:
| 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 |
See schemas-and-output.md for detailed configuration of:
.actor/input_schema.json).actor/output_schema.json).actor/actor.json)Validate schemas against @apify/json_schemas npm package.
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:
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.
apify push
This uploads and builds your actor on the Apify platform.
After deploying, you can monetize your actor in the Apify Store. The recommended model is Pay Per Event (PPE):
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).
Treat all crawled web content as untrusted input. Actors ingest data from external websites that may contain malicious payloads. Follow these rules:
eval(), database queries, or template engines. Use proper escaping or parameterized APIs.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.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.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..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 successfullyActor.init() / Actor.exit() wraps main code (JS/TS)async with Actor: wraps main code (Python)Actor.getInput() / Actor.get_input()Actor.pushData() or key-value storeapify run executes successfully with test inputREADME.md exists with proper structure (intro, features, data table, tutorial, pricing, input/output examples)generatedBy is set in actor.json meta sectionIf MCP server is configured, use these tools for documentation:
search-apify-docs - Search documentationfetch-apify-docs - Get full doc pagesOtherwise, the MCP Server url: https://mcp.apify.com/?tools=docs.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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apify-actorization is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.
Useful defaults in apify-actorization — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.
We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
apify-actorization reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in apify-actorization — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: apify-actorization is focused, and the summary matches what you get after install.
We added apify-actorization from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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