empirical-prompt-tuning

Run subagent evaluations to collect metrics and refine prompts iteratively.

Updated Nov 12, 2017

Search Tags:#workflow #metrics #evaluation #subagent #prompt-design #prompt-tuning #empirical

Ready to run

npx skills add https://github.com/ak110/dotfiles --skill empirical-prompt-tuning-ak110

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: empirical-prompt-tuning
Source: https://github.com/ak110/dotfiles/tree/main/.chezmoi-source/dot_claude/skills/empirical-prompt-tuning
Command: npx skills add https://github.com/ak110/dotfiles --skill empirical-prompt-tuning-ak110

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Empirical Prompt Tuning provides a structured workflow to evaluate and iteratively improve prompts by executing them with a subagent, collecting objective metrics, and surfacing actionable refinements.

Core Features & Use Cases

Baseline and compare: establish a repeatable evaluation baseline for prompts and skills.
Bias removal: design prompts to minimize ambiguity and surface hidden assumptions.
Multi-scenario testing: run multiple task scenarios to detect edge cases and improve robustness.
Structured reporting: produce clear reports with success metrics, tool usage, duration, and retries to guide improvements.

Quick Start

Start an empirical evaluation cycle on the target skill to begin iterative refinement.

Frequently Asked Questions about empirical-prompt-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I empirically evaluate and refine prompts to improve task performance?▼

To empirically evaluate prompt tuning, you dispatch subagents to execute target tasks, collect objective metrics like tool usage, duration, and retries, and iteratively refine prompts based on structured reports. This establishes a repeatable baseline to measure improvements.

What metrics are used to measure prompt effectiveness during evaluation?▼

Prompt evaluation metrics include tool usage, task duration, retry counts, and critical failure tracking. These objective data points guide iterative improvements and surface actionable refinements for multi-scenario testing.

Can I test prompts across multiple scenarios to remove bias and detect edge cases?▼

Yes, multi-scenario testing runs various task scenarios to detect edge cases and improve robustness. Bias removal is achieved by designing prompts that minimize ambiguity and surface hidden assumptions across these different scenarios.

Do I need subagent dispatch capabilities to run empirical prompt tuning?▼

Yes, the empirical prompt tuning process requires the ability to dispatch subagents. It relies on subagent execution to measure tool usage, duration, retries, and track critical failures to guide iterative refinement.

What is the best way to establish a baseline when tuning prompts?▼

The best way to establish a baseline is by running an empirical evaluation cycle on the target skill. This produces structured reports with success metrics, allowing you to compare subsequent iterations objectively.