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empirical-prompt-tuning

Evaluate prompts and skills through structured empirical testing workflows.

1|Updated Mar 6, 2014
Search Tags:#claude code#skill testing#workflow validation#instruction design#agent evaluation#prompt tuning
One-click install
npx skills add https://github.com/79yuuki/dotfiles --skill empirical-prompt-tuning-79yuuki
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: empirical-prompt-tuning
Source: https://github.com/79yuuki/dotfiles/tree/main/claude/skills/empirical-prompt-tuning
Command: npx skills add https://github.com/79yuuki/dotfiles --skill empirical-prompt-tuning-79yuuki

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps prevent unreliable agent instructions by replacing subjective prompt reviews with structured empirical testing using fixed scenarios, checklists, and independent executors.

Core Features & Use Cases

  • Prompt Quality Evaluation: Tests skills, AGENTS.md files, CLAUDE.md files, slash commands, task prompts, and routing descriptions against predefined scenarios.
  • Iterative Improvement Workflow: Guides baseline creation, executor feedback collection, targeted revisions, and convergence checks for high-frequency or routing-sensitive instructions.
  • Use Case: Improve a frequently used coding agent skill by measuring ambiguity, checklist success, retries, and hold-out scenario performance before promoting it as a standing rule.

Quick Start

Use the empirical-prompt-tuning skill to evaluate and improve the instructions in the specified skill file with fixed scenarios and an independent executor.

Frequently Asked Questions about empirical-prompt-tuning

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

FAQPage Schema
What is empirical prompt testing for agent instructions?▼

Empirical prompt testing evaluates agent instructions like AGENTS.md files and task prompts against fixed scenarios and checklists to measure reliability improvements before adoption.

How do I validate a CLAUDE.md file before promoting it as a standing rule?▼

Validate a CLAUDE.md file by creating baseline scenarios, collecting independent executor feedback, tracking iterations, and checking convergence criteria to measure instruction reliability.

When should I use structured prompt tuning for my coding agent skills?▼

Use structured prompt tuning for high-frequency skills or routing-sensitive prompts that require validation before adoption to prevent unreliable agent behavior and reduce retries.

Does empirical prompt tuning work with slash commands and routing descriptions?▼

Yes, empirical prompt tuning tests slash commands, task prompts, and routing descriptions against predefined scenarios using independent executor feedback to evaluate instruction quality.

What components do I need for an iterative prompt improvement workflow?▼

An iterative prompt improvement workflow requires fixed scenarios, checklists, independent executor feedback, iteration tracking, and convergence criteria to measure reliability improvements.

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