Deploying Team Training¶
Get every teammate's AI automatically applying the same SQL conventions, naming standards, and anti-pattern rules. Achieved by committing .altimate-code/memory/ to git so that teammates inherit your training on git pull.
Step 1: Create Your First Team Training Entries¶
Use the /teach or /train skills to save project-specific conventions:
Verify the training was saved:
This shows all active training entries, their scope (global vs project), and when they were added.
Step 2: Locate the Training Files¶
Training is stored in .altimate-code/memory/ in your project root. Each entry is a markdown file with YAML frontmatter:
Global vs. project scope:
- Project scope (.altimate-code/memory/): Applies when working in this project. Commit to git to share with team.
- Global scope (~/.altimate-code/memory/): Applies across all projects. Do not commit, as this is personal.
Step 3: Commit to Git¶
git add .altimate-code/memory/
git commit -m "Add team SQL conventions and naming standards"
git push
Teammates who git pull automatically inherit all training entries. No additional setup is required because the tool reads from .altimate-code/memory/ on startup.
Step 4: Verify a Teammate Got the Training¶
After a teammate pulls, they can run:
They should see the same entries you created. If they don't, check that .altimate-code/memory/ is not in .gitignore.
Best Practices¶
What to teach first: 1. Your team's most common SQL mistakes (the things that keep coming up in code review) 2. Naming conventions for models, tables, and columns 3. Project-specific patterns: your medallion layer names, your warehouse, your dbt project structure
Handling conflicting corrections:
Later corrections override earlier ones for the same topic. Use /training-status to audit and delete stale entries with /forget <entry-id>.
Global vs. project scope: Use project scope for team standards. Use global scope only for personal preferences that apply to all your projects (e.g., preferred SQL style).
Limitations¶
Training is as good as the corrections you save. The system doesn't infer conventions from your existing codebase; you teach it explicitly. For the full description of how training works, see Training Overview.