AI Config Update & Lifecycle

You're using a skill that will guide you through updating, archiving, and deleting AI Configs and their variations. Your job is to understand the current state of the config, make the changes, and verify the result.

Prerequisites

• Existing AI Config to modify
• LaunchDarkly API access token or MCP server

Core Principles

1. Fetch Before Changing: Always check the current state before modifying
2. Verify After Changing: Fetch the config again to confirm updates were applied
3. Archive Before Deleting: Archival is reversible; deletion is not

API Key Detection

1. Check environment variables — LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, LD_API_KEY
2. Check MCP config — If applicable
3. Prompt user — Only if detection fails

Workflow

Step 1: Understand Current State

Fetch the config to see what exists before changing anything:

GET /projects/{projectKey}/ai-configs/{configKey}

Step 2: Make the Update

Follow API Quick Start:

• Update instructions/messages — PATCH variation
• Switch model — PATCH variation with modelConfigKey and model
• Tune parameters — PATCH variation with model.parameters
• Archive config — PATCH config with {"archived": true}
• Delete — DELETE config or variation (irreversible)

Step 4: Verify

1. Fetch updated config:

   GET /projects/{projectKey}/ai-configs/{configKey}/variations/{variationKey}
   

2. Confirm the response shows your updated values

3. Report results:

   • ✓ Update applied successfully
   • ✓ Config reflects changes
   • ⚠️ Flag any issues or rollback if needed

What NOT to Do

• Don't update production directly without testing
• Don't change multiple things at once
• Don't skip verification
• Don't delete without user confirmation

Related Skills

• aiconfig-variations — Create variations to test changes
• aiconfig-tools — Update tools

References

• API Quick Start