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ax-cpp-refine

Refine C++ Ax generation workflows with reward-scored optimizer feedback loops.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you write and refine C++ code that uses the generated axllm package for iterative generation improvement, evaluator feedback, and optimizer-driven search without relying on TypeScript-only helpers.

Core Features & Use Cases

  • Reward-Scored Refinement: Build C++ workflows that score candidates and improve outputs across multiple optimization rounds.
  • Optimizer Integration: Use AxGEPA, bootstrap few-shot, and other optimizer surfaces when refining generated results.
  • Practical Use Case: Update a candidate generation pipeline so it can run an evaluator, compare outputs, and return the best improved result using native C++ APIs.

Quick Start

Use the ax-cpp-refine skill to adapt my C++ Ax workflow so it uses the generated axllm optimizer APIs and matches the package examples.

Frequently Asked Questions about ax-cpp-refine

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

FAQPage Schema
How do I implement reward-scored refinement for C++ generation workflows?▼

Reward-scored refinement in C++ generation workflows is implemented by building candidate scoring pipelines that improve outputs across multiple optimization rounds using evaluator feedback and optimizer-driven search APIs.

How do I port TypeScript refinement logic into native C++ APIs?▼

Porting refinement logic from TypeScript into generated C++ surfaces requires using the generated package APIs, package examples, and AxIR-aligned optimizer interfaces to build native evaluator feedback loops without TypeScript-only helpers.

Can I use AxGEPA and bootstrap few-shot optimizers in my C++ Ax workflow?▼

Yes, you can integrate AxGEPA, bootstrap few-shot, and other optimizer surfaces into C++ Ax workflows to run evaluators, compare generated outputs, and return the best improved result using native APIs.

What do I need to set up before refining C++ Ax generation workflows?▼

Refining C++ Ax generation workflows requires generated package APIs, package examples, capability manifests, AxIR-aligned optimizer interfaces, and deterministic no-key or provider-backed execution paths to function properly.

What's the best way to run evaluator feedback loops in C++ without relying on TypeScript helpers?▼

The best way to run evaluator feedback loops natively in C++ is to use the generated axllm package APIs to execute deterministic no-key or provider-backed paths that compare outputs and select the best candidate.

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