apify__agent-skills/commands/create-actor.md

description, argument-hint

description	argument-hint
Guided Apify Actor development with best practices and systematic workflow	Optional actor description

Actor Development

You are helping a developer create an Apify Actor - a serverless cloud program for web scraping, automation, and data processing. Follow a systematic approach: understand requirements, configure environment, design architecture, implement, test, and deploy.

Core Principles

• Ask clarifying questions: Identify target websites, data requirements, edge cases, and constraints before implementation
• Follow Apify best practices: Use appropriate crawlers (Cheerio vs Playwright), implement proper error handling, respect rate limits
• Validate early: Check CLI installation and authentication before starting
• Use TodoWrite: Track all progress throughout
• Security first: Use apify/log for censoring sensitive data, validate input, handle errors gracefully

---

Phase 1: Discovery

Goal: Understand what actor needs to be built

Initial request: $ARGUMENTS

Actions:

1. Create todo list with all phases
2. Ask user for clarification if needed:
   • What is the actor's primary purpose? (web scraping, automation, data processing)
   • What websites/services will it interact with?
   • What data should it extract or what actions should it perform?
   • Any specific requirements or constraints?
3. Summarize understanding and confirm with user

---

Phase 2: Environment Setup

Goal: Verify Apify CLI is installed and authenticated

CRITICAL: Do not proceed without proper setup

Actions:

1. Check if Apify CLI is installed: apify --help
2. If not installed, guide user to install:

   curl -fsSL https://apify.com/install-cli.sh | bash
   # Or: brew install apify-cli (Mac)
   # Or: npm install -g apify-cli
   

3. Verify authentication: apify info
4. If not logged in:
   • Check for APIFY_TOKEN environment variable
   • If missing, ask user to generate token at https://console.apify.com/settings/integrations
   • Login with: apify login -t $APIFY_TOKEN

---

Phase 3: Language Selection

Goal: Choose programming language and template

Actions:

1. Ask user which language they prefer:
   • JavaScript (skills/apify-actor-development/references/actor-template-js.md)
   • TypeScript (skills/apify-actor-development/references/actor-template-ts.md)
   • Python (skills/apify-actor-development/references/actor-template-python.md)
2. Note: Additional packages (Crawlee, Playwright, etc.) can be installed later as needed

---

Phase 4: Requirements & Architecture Design

Goal: Define input/output schemas and implementation approach

Actions:

1. Clarify detailed requirements:
   • What input parameters should the actor accept?
   • What output format is needed? (dataset items, key-value store files, both)
   • Should it use CheerioCrawler (10x faster for static HTML) or PlaywrightCrawler (for JavaScript-heavy sites)?
   • Concurrency settings? (HTTP: 10-50, Browser: 1-5)
   • Rate limiting and retry strategies?
   • Should standby mode be enabled?
2. Design architecture:
   • Input schema structure
   • Output/dataset schema structure
   • Key-value store schema (if needed)
   • Error handling approach
   • Data validation and cleaning strategy
3. Present architecture to user and get approval

---

Phase 5: Actor Creation

Goal: Create actor from template and configure schemas

DO NOT START WITHOUT USER APPROVAL

Actions:

1. Wait for explicit user approval
2. Copy appropriate language template from skills/apify-actor-development/references/ directory
3. Update .actor/actor.json:
   • Set actor name and version
   • IMPORTANT: Fill in generatedBy property with current model name
   • Configure runtime, memory, timeout
   • Set usesStandbyMode if applicable
4. Create/update .actor/input_schema.json with input parameters
5. Create/update .actor/output_schema.json with output structure
6. Create/update .actor/dataset_schema.json if using datasets
7. Create/update .actor/key_value_store_schema.json if using key-value store
8. Update todos as you progress

Reference documentation:

• skills/apify-actor-development/references/actor-json.md
• skills/apify-actor-development/references/input-schema.md
• skills/apify-actor-development/references/output-schema.md
• skills/apify-actor-development/references/dataset-schema.md
• skills/apify-actor-development/references/key-value-store-schema.md

---

Phase 6: Implementation

Goal: Implement actor logic following best practices

Actions:

1. Implement actor code in src/main.py, src/main.js, or src/main.ts
2. Follow best practices:
   • ✓ Use Apify SDK (apify) for code running on Apify platform
   • ✓ Validate input early with proper error handling
   • ✓ Use CheerioCrawler for static HTML (10x faster)
   • ✓ Use PlaywrightCrawler only for JavaScript-heavy sites
   • ✓ Use router pattern for complex crawls
   • ✓ Implement retry strategies with exponential backoff
   • ✓ Use proper concurrency settings
   • ✓ Clean and validate data before pushing to dataset
   • ✓ Always use apify/log package - censors sensitive data
   • ✓ Implement readiness probe handler if using standby mode
   • ✗ Don't use browser crawlers when HTTP/Cheerio works
   • ✗ Don't hard code values that should be in input schema
   • ✗ Don't skip input validation or error handling
   • ✗ Don't overload servers - use appropriate concurrency and delays
3. Implement standby mode readiness probe if usesStandbyMode: true (see skills/apify-actor-development/references/standby-mode.md)
4. Use proper logging (see skills/apify-actor-development/references/logging.md)
5. Update todos as you progress

---

Phase 7: Documentation

Goal: Create comprehensive README for marketplace

Actions:

1. Create README.md with:
   • Clear description of what the actor does
   • Input parameters with examples
   • Output format with examples
   • Usage instructions
   • Limitations and known issues
   • Example runs
2. Include code examples for common use cases
3. Mention rate limits, costs, or legal considerations if applicable

---

Phase 8: Local Testing

Goal: Test actor locally before deployment

Actions:

1. Install dependencies:
   • JavaScript/TypeScript: npm install
   • Python: pip install -r requirements.txt
2. Create test input file at storage/key_value_stores/default/INPUT.json with sample parameters
3. Run actor locally: apify run
4. Verify:
   • Input is parsed correctly
   • Actor completes successfully
   • Output is in expected format
   • Error handling works
   • Logging is appropriate
5. Fix any issues found
6. Test edge cases and error scenarios

---

Phase 9: Deployment

Goal: Deploy actor to Apify platform

DO NOT DEPLOY WITHOUT USER APPROVAL

Actions:

1. Ask user if they want to deploy now
2. If yes, deploy with: apify push
3. Actor will be deployed with name from .actor/actor.json
4. Provide user with:
   • Deployment confirmation
   • Actor URL on Apify platform
   • Instructions for running on platform

---

Phase 10: Summary

Goal: Document what was accomplished

Actions:

1. Mark all todos complete
2. Summarize:
   • What actor was built
   • Key features and capabilities
   • Input/output schemas
   • Files created/modified
   • Deployment status
   • Suggested next steps (testing on platform, publishing to store, monitoring)

---

Additional Resources

MCP Tools (if configured):

• search-apify-docs - Search documentation
• fetch-apify-docs - Get full doc pages

Documentation:

• docs.apify.com/llms.txt - Apify quick reference
• docs.apify.com/llms-full.txt - Apify complete docs
• crawlee.dev/llms.txt - Crawlee quick reference
• crawlee.dev/llms-full.txt - Crawlee complete docs
• whitepaper.actor - Complete Actor specification