Plugin Patterns

Canvas Plugin Patterns

This skill provides architectural patterns, best practices, and implementation guidance for building Canvas plugins. It supplements the SDK reference with practical patterns learned from real-world plugins.

When to Use This Skill

Use this skill when you need:

• Plugin architecture recommendations
• Implementation patterns and templates
• Best practices for testing, error handling, security
• Guidance on plugin complexity decisions
• Common anti-patterns to avoid
• Implementation of:
  • AWS S3
  • LLM (Anthropic Claude, OpenAI ChatGPT, Google Gemini)
  • Twilio
  • SendGrid
  • Extend.ai

Quick Reference

Reference the patterns_context.txt file for detailed patterns and examples.

Effects are applied only after the handler returns, as a single batch capped at 64 MB (gRPC). A handler that emits effects proportional to an unbounded queryset (all providers/patients/appointments) can exceed that ceiling, and the entire batch is silently dropped — the plugin logs the work as done but nothing lands. Scaling the worker does not fix this; it is a batching problem. Chunk large fan-out through a queue + cron (one small effect batch per invocation). See the database-performance skill (§"Canvas Execution Limits") for the full pattern, plus the write-amplification and over-hydration/memory failure modes that don't show up as N+1.