Apify Actorization

Apify Actorization

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/apify-actorization from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prerequisites, Actorization Checklist, Monetization (Optional), Pre-Deployment Checklist, Apify MCP Tools, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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
• Use when the request clearly matches the imported source intent: 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,....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

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?
6. .actor/actor.json - Actor configuration and metadata
7. .actor/input_schema.json - Input definition for the Apify Console

Imported Workflow Notes

Imported: 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?

Imported: 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

Imported: 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

Imported: 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.

Imported: Step 4: 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.

Imported: Step 5: Deploy

apify push

This uploads and builds your actor on the Apify platform.

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @apify-actorization to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @apify-actorization against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @apify-actorization for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @apify-actorization using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/apify-actorization, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @20-andruia-niche-intelligence - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @advogado-criminal - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @advogado-especialista - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @agent-memory-systems - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• cli-actorization.md
• js-ts-actorization.md
• python-actorization.md
• schemas-and-output.md
• cli-actorization.md
• js-ts-actorization.md

Imported Reference Notes

Imported: 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

Imported: 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

Imported: 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).

Imported: 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

Imported: 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.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.