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agentsop-prompt-compilation

Verify metric readiness and data floors before DSPy optimizer compilation.

287|16|Updated May 20, 2026
Search Tags:#prompt optimization#data validation#cost control#dspy#evaluation metrics#optimizer selection
One-click install
npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-prompt-compilation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentsop-prompt-compilation
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-prompt-compilation
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-prompt-compilation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It prevents wasting expensive prompt-optimization runs by ensuring you only compile an optimizer when you have a validated metric and enough labeled data for the optimizer you intend to run.

Core Features & Use Cases

  • Two-gate readiness decision: checks that the metric exists and is human-validated (at least 20 spot-checks) and that the labeled example count clears the floor for the chosen optimizer.
  • Optimizer selection by constraints: picks an optimizer based on data scale and whether you have textual feedback (e.g., GEPA can work with far fewer examples when feedback is rich).
  • Cost discipline and escalation rules: starts with a cheap probe (auto="light"), escalates only on meaningful lift, and confirms gains on a held-out test set rather than the optimization validation set.

Quick Start

Ask your coding/agent workflow to decide whether it is compile-ready for DSPy prompt auto-optimization, returning a go/no-go gate and the recommended optimizer choice.

Frequently Asked Questions about agentsop-prompt-compilation

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

FAQPage Schema
What do I need to verify before running DSPy prompt auto-optimization?▼

DSPy prompt optimization readiness requires a human-validated metric, verified through at least 20 spot-checks, and a sufficient number of labeled examples meeting the floor for your chosen optimizer.

How does DSPy optimizer selection work with limited labeled data?▼

DSPy optimizer selection evaluates your data scale and textual feedback richness, allowing optimizers like GEPA to function with far fewer examples when feedback is detailed.

Can I control compute costs when compiling DSPy optimizers?▼

Yes, cost control starts with a cheap probe run using auto="light", escalating only when meaningful lift is detected and confirming gains on a held-out test set.

When should I run DSPy prompt compilation instead of manual prompting?▼

You should run DSPy prompt compilation when manual prompting plateaus, using a two-gate readiness check to ensure you have validated metrics and enough data before spending compute.

What happens if my evaluation metric fails the spot-check validation?▼

If your evaluation metric fails spot-check validation, the compile-ready gate blocks the optimization run, preventing wasted compute on an unverified metric.

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