Config Variations

You're using a skill that will guide you through testing and optimizing configs through variations. Your job is to design experiments, create variations, and systematically find what works best.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Primary MCP tool:

• clone-ai-config-variation -- clone a baseline variation with selective overrides (recommended for experimentation)

Alternative MCP tools (for more control):

• get-ai-config -- review existing variations before adding new ones
• create-ai-config-variation -- create new variations from scratch

Optional MCP tools:

• update-ai-config-variation -- refine a variation after creation
• delete-ai-config-variation -- remove variations that didn't work out

Core Principles

1. Test One Thing at a Time: Change model OR prompt OR parameters, not all at once
2. Have a Hypothesis: Know what you're trying to improve
3. Measure Results: Use metrics to compare variations
4. Verify via Tool: The agent fetches the config to confirm variations exist

Workflow

Step 1: Identify What to Optimize

What's the problem? Cost, quality, speed, accuracy? How will you measure success?

Step 2: Design the Experiment

Step 3: Create Variations (Recommended: Clone with Overrides)

Use clone-ai-config-variation to duplicate the baseline and override only what you're testing. The tool reads the source variation, merges your overrides, and creates the new variation. Everything you don't pass is inherited from the source automatically.

Required fields:

• sourceVariationKey -- the baseline to clone from
• key and name -- identifiers for the new variation (e.g., gpt4o-mini-cost-test)

Override ONLY the fields you are testing. Leave all other fields unset -- do not pass them even if you know their current values. The clone tool inherits them from the source. This enforces the one-variable-at-a-time principle:

• Testing a cheaper model? Pass only modelConfigKey and modelName. Do NOT pass instructions, messages, or parameters.
• Testing different instructions? Pass only instructions. Do NOT pass modelConfigKey or modelName.
• Testing a parameter? Pass only parameters. Do NOT pass model or prompt fields.

The response returns both the source and created variation, so you can immediately verify the diff.

Step 3 (Alternative): Create from Scratch

If you need full control, use get-ai-config first to review the current state, then create-ai-config-variation with all fields specified manually. Always fetch before creating so you understand the existing config's mode, model, and parameters.

Step 4: Verify

If you used clone-ai-config-variation, the response includes both source and created variations for immediate comparison. Otherwise, use get-ai-config to confirm.

Report results:

• Variations created with correct models and parameters
• Only the intended variable differs between variations
• Flag any issues

Note on API responses: After calling a creation or clone tool, treat a successful response as confirmation that the operation succeeded. The API response may not echo back every field you sent (e.g., model fields may show defaults). Do not retry or assume failure based on response field values alone -- verify with get-ai-config if needed.

modelConfigKey Format

Required for models to display in the UI. Format: {Provider}.{model-id}:

• OpenAI.gpt-4o, OpenAI.gpt-4o-mini
• Anthropic.claude-sonnet-4-5, Anthropic.claude-3-5-sonnet

Safety: Protect the Baseline

When the user wants to try a different model, prompt, or parameters, always create a new variation alongside the baseline. Never modify or delete the existing baseline variation. This applies even if the user says "replace" or "switch" -- the correct action is to create a new variation and let targeting/rollouts control traffic, not to edit the original.

• Use clone-ai-config-variation or create-ai-config-variation to add the new variation
• Do NOT use update-ai-config-variation on the baseline to change its model or instructions
• Do NOT use delete-ai-config-variation on the baseline
• Explain to the user that keeping the baseline enables comparison and safe rollback

What NOT to Do

• Don't test too many things at once -- change one variable per variation
• Don't pass unchanged fields when cloning -- let the tool inherit them from the source
• Don't forget modelConfigKey (variations without it show as "NO MODEL" in the UI)
• Don't make decisions on small sample sizes
• Don't modify or remove the baseline variation -- create new variations alongside it
• Don't use update-ai-config-variation to "replace" a baseline -- create a new variation instead

More resources

To learn more about creating and managing variations, read Create and manage config variations.

Related Skills

• configs-create -- Create the initial config
• configs-update -- Refine based on learnings