empirical-prompt-tuning
CommunityIteratively test prompts with unbiased agents.
Education & Research#ai-agents#structured-report#iterative-improvement#prompt-evaluation#subagent-dispatch#bias-removal
AuthorHyd3-14
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Prompt quality is often hard to judge; this skill enables unbiased evaluation by having automated agents execute prompts and report actionable metrics, reducing bias in assessment.
Core Features & Use Cases
- Iterative evaluation workflow: baseline setup, scenario design, and quantitative scoring to drive prompt improvements.
- Bias-aware testing: isolates instruction quality from author bias by using neutral evaluators and structured feedback.
- Comprehensive reporting: returns a structured assessment including qualitative notes, automation traces, and retrial data.
Quick Start
Trigger an empirical evaluation by dispatching a subagent to run the target prompt across defined scenarios and return a structured report.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: empirical-prompt-tuning Download link: https://github.com/Hyd3-14/dotfiles/archive/main.zip#empirical-prompt-tuning Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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