Ax Refinement Patterns For C++
Use when writing C++ code with axllm for reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns.
Install
Install only this skill for C++:
npx skills add https://ax-llm.github.io/ax/cpp/ --skill 'ax-cpp-refine'Published skill file: ax-cpp-refine/SKILL.md.
Source
- Source: packages/cpp/skills/ax-cpp-refine/SKILL.md
- Version:
22.0.3
Skill Instructions
This skill helps an agent write C++ code with the generated Ax package axllm. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.
When To Use
- Improve generated outputs with evaluator feedback or optimizer artifacts.
- Port TypeScript refinement intent into generated-language surfaces without assuming TypeScript-only helpers.
- Use generated optimizer APIs when the target package does not expose a standalone refine helper.
Package Facts
- Language: C++.
- Package:
axllm. - Package API docs:
API.mdandaxir-api.json. - Capability manifest:
axir-capabilities.json. - Runnable examples:
examples/. - Real network support: yes.
- Scripted no-key transport support: yes.
- Runtime profiles:
javascript-quickjs,python-pyodide.
Core Pattern
axllm::AxGEPA engine(reflection_client, options);
auto result = engine.optimize(request, evaluator);Relevant API Surface
- AxGen:
axllm::ax,axllm::AxGen - Optimizers:
axllm::optimize,axllm::AxBootstrapFewShot,axllm::AxGEPA,axllm::OptimizerEngine,axllm::OptimizerEvaluator
Guardrails
- Start from package examples for exact native syntax before inventing a new call shape.
- Use
provider-apiexamples only when the user explicitly has provider credentials available. - Use
no-keyexamples for deterministic local checks and provider request mapping. - Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
- Do not copy repo-maintainer skills from
tools/*/skills/into user packages.