npx skills add ...
npx skills add launchdarkly/agent-skills --skill configs-variations
npx skills add launchdarkly/agent-skills --skill configs-variations
Experiment with configs by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.
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.
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 onescreate-ai-config-variation -- create new variations from scratchOptional MCP tools:
update-ai-config-variation -- refine a variation after creationdelete-ai-config-variation -- remove variations that didn't work outWhat's the problem? Cost, quality, speed, accuracy? How will you measure success?
| Goal | What to Vary |
|---|---|
| Reduce cost | Cheaper model (e.g., gpt-4o-mini) |
| Improve quality | Better model or more detailed prompt |
| Reduce latency | Faster model, lower max_tokens |
| Increase accuracy | Different model family (Claude vs GPT-4) |
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 fromkey 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:
modelConfigKey and modelName. Do NOT pass instructions, messages, or parameters.instructions. Do NOT pass modelConfigKey or modelName.parameters. Do NOT pass model or prompt fields.The response returns both the source and created variation, so you can immediately verify the diff.
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.
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:
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.
Required for models to display in the UI. Format: {Provider}.{model-id}:
OpenAI.gpt-4o, OpenAI.gpt-4o-miniAnthropic.claude-sonnet-4-5, Anthropic.claude-3-5-sonnetWhen 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.
clone-ai-config-variation or create-ai-config-variation to add the new variationupdate-ai-config-variation on the baseline to change its model or instructionsdelete-ai-config-variation on the baselineupdate-ai-config-variation to "replace" a baseline -- create a new variation insteadconfigs-create -- Create the initial configconfigs-update -- Refine based on learnings