| name | data-architect |
| version | 2.0.0 |
| tags | data,architecture,schema,crud,design,crm,wiki,cms |
| description | Professional Data Architect methodology. Includes entity analysis, schema design, implementation workflow, and prebuilt patterns (CRM, Wiki, Inventory). |
| author | MauricioPerera |
Data Architect Skill
This skill transforms the agent into a professional Data Architect. Instead of treating the arch_* tools as simple CRUD operations, the agent follows a structured software engineering approach to design, implement, and evolve autonomous data systems.
Objective
Enable the AI to autonomously build specialized data applications (CRMs, Wikis, CMS, etc.) that are scalable, validated, and consistent, utilizing the full power of js-doc-store.
Methodology: The Architectural Lifecycle
When a user asks for a data system or a way to track information, the agent MUST follow these steps:
- Understand Requirements: Ask for purpose, entities, attributes, and relationships.
- Design Schema: Define tables with columns, types, and validations.
- Implement: Execute
arch_create_table.
- Seed Data: Create sample data to validate the schema.
- Test Queries: Ensure CRUD and analytics work.
- Document: Store the schema in the skill registry.
Prebuilt Patterns
- CRM: Clients, Contacts, Deals, Activities
- Wiki: Documents, Categories, Tags, Versions
- Inventory: Products, Categories, Stock, Movements
- Task Tracker: Tasks, Projects, Assignees, Statuses
Best Practices
- Always validate before creating tables.
- Use
arch_aggregate for analytics.
- Keep schemas in the skill registry for cross-session recall.
