zai-automation

name	zai-automation
description	Frameworks for automating business workflows using AI, APIs, and integration platforms like n8n or Zapier.

Language and Coding Standards

• Communication: Always talk in Thai when interacting with users.
• Code & Technical Assets: All code, comments, documentation, and technical definitions must be in English.

AI Automation Skill

ZeaZ Platform & apps/* Monorepo Rules

When implementing tasks on the zeaz-platform repository, you MUST strictly enforce these architecture and workflow rules:

1. Monorepo Architecture (apps/*): The platform is a unified monorepo. ALL applications, microservices, frontends, and AI toolings (e.g., zLinebot, zwallet, zdash) reside inside the apps/ directory. Do not create top-level directories for apps. When refactoring or adding features, always scope your work to the specific apps/<app-name>/ folder.
2. Environment Variables: Avoid scattering .env files. Consolidate environment variables into a central .env.example inside the respective app folder. Canonical Cloudflare variables (e.g. CLOUDFLARE_API_TOKEN, CLOUDFLARE_ZONE_ID) MUST be used instead of legacy CF_ variants.
3. Commit Workflow: NEVER use git commit or git push directly. ALWAYS stage your intended files with git add and commit using make gpg-finalize COMMIT_MSG="..." from the repository root to ensure all GitOps and DevSecOps checks pass.
4. Security: NEVER commit or generate real secrets. Unsafe placeholders like test-secret-value-value-value, test-secret-value-value-value, test-secret-value-value-value are FORBIDDEN.
5. Language: Code, documentation, and technical definitions MUST be in English.

1. Overview

AI Automation combines traditional trigger-action workflows (via tools like n8n, Make, or Zapier) with LLMs to handle cognitive tasks such as categorization, extraction, and drafting responses.

2. Core Components of an Automation

• Trigger: The event that starts the workflow (e.g., New Email, Webhook received, Scheduled time).
• Nodes / Steps: The actions taken (e.g., Fetch data from API, Format text).
• AI Cognitive Node: A step where an LLM processes unstructured data (e.g., "Extract the invoice total and vendor name from this PDF").
• Destination: Where the final data is sent (e.g., Save to Notion, Send Slack message).

3. Best Practices

• Error Handling & Fallbacks: APIs fail. Always include error-catching nodes (try/catch equivalents) and notifications for failed runs.
• Idempotency: Ensure that if a workflow runs twice on the same data by accident, it doesn't cause duplicate side-effects.
• Structured AI Outputs: Always force the AI node to return strict JSON using JSON schemas to ensure the next automation step doesn't break.
• Cost Management: Monitor LLM token usage within loops. Do not put an expensive LLM call inside a loop processing thousands of low-value items.

4. Key Automation Platforms

• n8n: Powerful, node-based, self-hostable.
• Make.com: Visual, flexible for complex branching.
• Zapier: Easiest integration, widely supported, but rigid.