run-lightdash-evals

name: run-lightdash-evals description: Orchestrate evaluation runs and test case management for Lightdash agents.

Run Lightdash Evaluations

Skill for managing and executing evaluations for Lightdash AI agents.

Purpose

Enables the "Eval-Driven Development" workflow by providing tools to create evaluation suites, append test cases (prompts), execute evaluation runs, and analyze the results.

Tools

Wraps the following MCP tools from the lightdash-tools server:

• ldt__list_agent_evaluations
• ldt__get_agent_evaluation
• ldt__create_agent_evaluation
• ldt__update_agent_evaluation
• ldt__append_agent_evaluation_prompts
• ldt__run_agent_evaluation
• ldt__list_agent_evaluation_runs
• ldt__get_agent_evaluation_run_results
• ldt__delete_agent_evaluation

Safety Mode Compliance

• Read Tools: list_agent_evaluations, get_agent_evaluation, list_agent_evaluation_runs, get_agent_evaluation_run_results.
• Write-Safe Tools: create_agent_evaluation, update_agent_evaluation, append_agent_evaluation_prompts, run_agent_evaluation.
• Write-Destructive Tools: delete_agent_evaluation.

Behavior

1. Test Case Management:
   • Use ldt__append_agent_evaluation_prompts to add 20-50 diverse test cases representing real-world user queries.
   • Organize evaluations by agent or project to maintain clarity.
2. Execution:
   • Trigger a run using ldt__run_agent_evaluation.
   • Monitor the progress using ldt__list_agent_evaluation_runs.
3. Analysis:
   • Once a run is complete, fetch the detailed results via ldt__get_agent_evaluation_run_results.
   • Identify patterns in failures (e.g., specific dimensions or metrics that the agent struggles with).

Rules

• ALWAYS create or update an evaluation suite before deploying major changes to an agent's prompt.
• NEVER delete an evaluation suite without explicit confirmation.
• Use the agent-tuner sub-agent to automatically process evaluation results for improvement.