figma_mcp_server_actor: Instructions file for Codex

What are Apify Actors?

• Actors are serverless cloud programs that can perform anything from a simple action, like filling out a web form, to a complex operation, like crawling an entire website or removing duplicates from a large dataset.
• Actors are programs packaged as Docker images, which accept a well-defined JSON input, perform an action, and optionally produce a well-defined JSON output.

Apify Actor directory structure

.actor/
├── actor.json # Actor config: name, version, env vars, runtime settings
├── input_schema.json # Input validation & Console form definition
├── dataset_schema.json # Dataset schema definition
└── output_schema.json # Specifies where an Actor stores its output
src/
└── main.js # Actor entry point and orchestrator
storage/ # Local storage (mirrors Cloud during development)
├── datasets/ # Output items (JSON objects)
├── key_value_stores/ # Files, config, INPUT
└── request_queues/ # Pending crawl requests
Dockerfile # Container image definition
AGENTS.md # AI agent instructions (this file)

Apify CLI

Installation

• Install Apify CLI only if it is not already installed.
• If Apify CLI is not installed, install it using the following commands:
  • macOS/Linux: curl -fsSL https://apify.com/install-cli.sh | bash
  • Windows: irm https://apify.com/install-cli.ps1 | iex

Apify CLI Commands

# Local development
apify run                              # Run Actor locally

# Authentication & deployment
apify login                            # Authenticate account
apify push                             # Deploy to Apify platform

# Help
apify help                             # List all commands

Do

• use the default values for all fields in the actor.json, input_schema.json, output_schema.json, and main.js files
• use Apify CLI to run the Actor locally, and push it to the Apify platform
• accept well-defined JSON input and produce structured JSON output
• use Apify SDK (apify) for code running ON Apify platform
• validate input early with proper error handling and fail gracefully
• use CheerioCrawler for static HTML content (10x faster than browsers)
• use PlaywrightCrawler only for JavaScript-heavy sites and dynamic content
• use router pattern (createCheerioRouter/createPlaywrightRouter) for complex crawls
• implement retry strategies with exponential backoff for failed requests
• use proper concurrency settings (HTTP: 10-50, Browser: 1-5)
• set sensible defaults in .actor/input_schema.json for all optional fields
• set up output schema in .actor/output_schema.json
• clean and validate data before pushing to dataset
• use semantic CSS selectors and fallback strategies for missing elements
• respect robots.txt, ToS, and implement rate limiting with delays
• check which tools (cheerio/playwright/crawlee) are installed before applying guidance