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prompt-optimizer

Optimize failing skill prompts with DSPy and write results to SKILL.md.

Updated Apr 10, 2026
Search Tags:#automation#skill-management#few-shot#prompt-optimization#dspy#teleprompter
One-click install
npx skills add https://github.com/Exia-thd/Digital-Nervous --skill prompt-optimizer-exia-thd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/Exia-thd/Digital-Nervous/tree/main/skills/prompt-optimizer
Command: npx skills add https://github.com/Exia-thd/Digital-Nervous --skill prompt-optimizer-exia-thd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Prompt Optimizer reduces the cycle time and waste in refining skill prompts by algorithmically optimizing prompts and few-shot examples using the DSPy framework, translating subjective improvements into verifiable, compiled LLM instructions.

Core Features & Use Cases

  • Automated Prompt Optimization: Rewrites failing skill prompts with deterministic DSPy-based planning and metrics.
  • Deterministic Evaluation: Uses a defined metric to validate plan quality and compile optimized prompts.
  • Migration & Extension: Helps migrate static SKILL.md logic into dynamic dspy.Module implementations and auto-generate new examples for new skills.
  • Use Case: When a skill's plan quality loops flag failures, DSPy optimization rebuilds prompts to maximize pass rates.

Quick Start

Run the DSPy teleprompter on a failing skill to produce an optimized prompt and update its SKILL.md with the new few-shot examples.

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
How does DSPy prompt optimization improve failing skill prompts?▼

DSPy prompt optimization improves failing skill prompts by using a teleprompter to compile instructions and auto-generate few-shot examples, applying a deterministic metric to validate plan quality and maximize pass rates.

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

Migrating static SKILL.md logic to dynamic DSPy modules involves algorithmically translating subjective prompt improvements into verifiable instructions, automatically generating few-shot examples, and writing optimized results back into the file.

How do I fix skill prompts stuck in plan-quality loop failures?▼

Fix skill prompts stuck in plan-quality loop failures by running a DSPy teleprompter with a defined deterministic metric to evaluate and rebuild the instructions, updating the target SKILL.md with optimized few-shot examples.

Can I automatically generate few-shot examples for new skills using DSPy?▼

Yes, you can automatically generate few-shot examples for new skills using the DSPy framework, which uses deterministic evaluation to compile validated examples and writes them directly back into your target skill configuration.

Does prompt-optimizer require manual metric definition to compile prompts?▼

Yes, effective prompt-optimizer compilation requires defining a deterministic metric to validate plan quality, ensuring the DSPy teleprompter can algorithmically evaluate and maximize pass rates for failing skills.

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