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prompt-optimizer

Automate end-to-end prompt optimization for failing skills using DSPy.

Updated Mar 25, 2026
Search Tags:#automation#workflow#few-shot#prompt-optimization#dspy#skill-migration
One-click install
npx skills add https://github.com/ouakar/ubinarys-dental --skill prompt-optimizer-ouakar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/ouakar/ubinarys-dental/tree/main/skills/forgewright/skills/prompt-optimizer
Command: npx skills add https://github.com/ouakar/ubinarys-dental --skill prompt-optimizer-ouakar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end prompt optimization for failing skills using DSPy to maximize pass rates.

Core Features & Use Cases

  • Automatic DSPy-based prompt synthesis and few-shot selection to improve plan quality.
  • Transforms static SKILL.md logic into dynamic DSPy Module representations for scalable maintenance.
  • Evaluates execution traces and applies deterministic metrics to drive prompt improvements and re-save results to SKILL.md.

Quick Start

Provide a DSPy-driven prompt optimization for a failing skill and save the compiled result back to SKILL.md.

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
How do I automate prompt optimization for failing skills using DSPy?▼

Automating prompt optimization with DSPy involves synthesizing prompts and selecting few-shot examples to improve execution pass rates. This skill evaluates execution traces against deterministic metrics to drive improvements and persist optimized results back to SKILL.md.

What is the best way to migrate static SKILL.md logic into DSPy modules?▼

Migrating static SKILL.md logic into DSPy modules transforms fixed instructions into dynamic representations for scalable maintenance. The process rewrites logic into executable modules to enable automated few-shot selection and continuous prompt synthesis.

Do I need a deterministic metric to use DSPy for prompt optimization?▼

Yes, a deterministic metric is required to validate prompt improvements during DSPy optimization. The metric evaluates execution traces to ensure the automated prompt synthesis effectively maximizes pass rates before saving the compiled result.

Can I persist optimized prompts directly back to SKILL.md after DSPy compilation?▼

Yes, optimized prompts can be persisted directly back to SKILL.md after DSPy compilation. The skill evaluates execution traces, applies deterministic metrics to drive improvements, and re-saves the validated prompt logic automatically.

When should I use automated few-shot selection for prompt optimization?▼

Automated few-shot selection should be used when a skill fails or is flagged by plan-quality-loop. It automatically synthesizes prompts using DSPy to maximize pass rates by evaluating execution traces against deterministic metrics.

Does prompt-optimizer require a specific environment to run DSPy prompt synthesis?▼

Yes, prompt-optimizer requires a DSPy-enabled environment to run automated prompt synthesis. You must have the framework configured to evaluate execution traces and apply deterministic metrics to drive prompt improvements.

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