empirical-prompt-tuning

Empirically refine agent prompts by running iterative evaluation loops with isolated subagents that test scenarios, collect metrics, and surface ambiguities until performance stabilizes. The process dispatches subagents to execute the target prompt, measure results against scoring rules, and report findings for the next iteration.

Empirical prompt tuning is a structured approach to improving AI agent instructions by running automated testing loops. It dispatches subagents to execute target prompts across scenarios, collects self-reports and tool usage metrics, and guides iterative refinements until performance metrics converge.

Test prompt safety during evaluation by using isolated subagents to run scenarios, preventing prompt injection and data leakage. The skill enforces safe execution boundaries, hold-out scenarios, and reproducible scoring to ensure secure and repeatable prompt testing.

Yes, empirical evaluation applies to skill prompts, slash commands, and task instructions across agents. The skill automates iterative testing and improvement for any agent prompt by applying clear scoring rules, thresholds, and reporting formats until metrics converge.

Detect ambiguities in LLM prompts by dispatching subagents to run scenarios and surface unclear instructions. The empirical evaluation loop identifies gaps in the prompt, collects performance metrics, and guides precise refinements to fill those gaps until the prompt stabilizes.

Agent prompts fail to converge during iterative testing when scoring rules lack clear thresholds or evaluation boundaries are inconsistent. The skill enforces reproducible scoring, hold-out scenarios, and safe isolated subagents to ensure metrics stabilize across repeated empirical loops.