teach

Teach

Purpose

Learn a reusable pattern from an example file. The user shows you a well-written artifact (model, query, config), and you extract the patterns worth following.

Workflow

1. Identify the file: The user provides a file reference (e.g., @models/staging/stg_orders.sql). Read the file.

2. Analyze patterns: Extract the structural patterns, NOT the specific content. Focus on:

   • File structure and organization (sections, ordering)
   • Naming conventions (prefixes, suffixes, casing)
   • SQL patterns (CTE vs subquery, join style, column ordering)
   • dbt conventions (materialization, tests, config blocks)
   • Common boilerplate (headers, comments, imports)
   • Data type choices
   • Error handling patterns

3. Present findings: Show the user what you learned in a structured list. Be specific:

   • Good: "Column order: keys first, then dimensions, then measures, then timestamps"
   • Bad: "Good column ordering"

4. Ask for confirmation: Let the user confirm, modify, or reject your findings before saving.

5. Save via training_save: Use the training_save tool with:

   • kind: "pattern"
   • name: A descriptive slug (e.g., "staging-model", "incremental-config")
   • content: The extracted patterns as a concise, actionable checklist
   • scope: "project" (default — shared with team via git)
   • source: The file path you learned from
   • citations: Reference to the source file

Important Guidelines

• Extract PATTERNS, not content. "Use {{ source() }} macro" is a pattern. "Query the orders table" is content.
• Keep it concise — max 10 bullet points per pattern. If more are needed, split into multiple patterns.
• Use the file's actual conventions, don't impose your own preferences.
• If the file doesn't have clear patterns worth learning, say so honestly.
• Do NOT make any LLM calls beyond the normal conversation flow — pattern extraction happens in your analysis, not via separate API calls.

Usage Examples