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ax-cpp-agent-optimize

Optimize C++ Ax agents with GEPA or BootstrapFewShot workflows.

2.9k|186|Updated Feb 23, 2023
Search Tags:#c++#agent optimization#evaluator#gepa#axllm#bootstrapfewshot
One-click install
npx skills add https://github.com/ax-llm/ax --skill ax-cpp-agent-optimize
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ax-cpp-agent-optimize
Source: https://github.com/ax-llm/ax/tree/main/website/static/cpp/.well-known/agent-skills/ax-cpp-agent-optimize
Command: npx skills add https://github.com/ax-llm/ax --skill ax-cpp-agent-optimize

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you optimize C++ Ax agents and reusable program components without guessing at the right evaluator, judge, or optimizer setup. It reduces trial-and-error when you need reproducible improvement runs, artifact persistence, and bounded execution budgets.

Core Features & Use Cases

  • Agent Optimization: Tune AxAgent behavior with GEPA or BootstrapFewShot workflows.
  • Evaluator and Judge Design: Build callbacks that score outputs consistently and safely.
  • Artifact Handling: Persist optimizer outputs for reuse, comparison, and later application.
  • Use Case: A team wants to improve a C++ agent that generates structured responses, so this Skill guides the optimization loop, evaluator wiring, and artifact storage in axllm.

Quick Start

Optimize the C++ AxAgent with axllm by defining an evaluator, setting explicit budgets, and running the appropriate optimizer with the package examples as your syntax source.

Frequently Asked Questions about ax-cpp-agent-optimize

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

FAQPage Schema
How do I optimize C++ agent workflows with bounded evaluation budgets?▼

You can optimize C++ agent workflows by applying bounded GEPA or BootstrapFewShot runs, which set explicit execution budgets to ensure reproducible improvement loops without uncontrolled resource consumption.

What is the best way to design evaluators and judges for Ax C++ agents?▼

Designing evaluators and judges for Ax C++ agents involves building callbacks that score outputs consistently and safely, reducing trial-and-error when wiring the optimization loop for reproducible performance gains.

How do I persist optimizer artifacts when tuning Ax C++ agents?▼

Persisting optimizer artifacts requires AxIR-aligned outputs and capability manifests, allowing you to store, compare, and later apply optimization results for reuse in axllm-based projects.

Do I need native C++ package APIs to run BootstrapFewShot optimization in axllm?▼

Yes, running BootstrapFewShot optimization requires native C++ package APIs and package examples to serve as your syntax source, along with no-key or provider-backed transport selection for the execution environment.

What problems does agent optimization solve for C++ programs generating structured responses?▼

Agent optimization solves the problem of unpredictable C++ agent behavior by guiding the optimization loop, standardizing evaluator wiring, and persisting artifacts, which eliminates guessing at the right evaluator or judge setup.

Can I use this approach to optimize reusable program components or only full agents?▼

You can optimize both full Ax C++ agents and reusable program components, applying GEPA or BootstrapFewShot workflows to tune behavior and improve performance across axllm-based projects.

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