| 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:
- 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.
- 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.
- 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.
- 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.
- 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.
