ax-cpp-typesafe

Typesafe / Jev For C++

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

• Use Jev for typed decisions with ordinary Ax signatures.
• Retain probabilities and scores through evidence-bearing AxGen signatures; use native questions for advanced criteria or structured state.
• Compose a separate generative program for prose or tools.

Package Facts

• Language: C++.
• Package: axllm.
• Package API docs: API.md and axir-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

auto model = axllm::ai("typesafe", axllm::object({{"api_key", api_key}, {"trueThreshold", 0.9}}));
auto triage = axllm::ax("ticket:string -> urgent:boolean(true \"Core task blocked\", false \"Routine request\"), team:class \"support, billing, engineering\"");
auto decision = triage.forward(*model, axllm::object({{"ticket", ticket}}));

Decision evidence

Add (evidence) to boolean/class/score/box signature outputs to retain probabilities, values, provenance, and native class/score confidence through AxGen. Boolean threshold 0.7 is local to that field; scalar fields still use provider trueThreshold. A plain score returns a continuous zero-based rubric index.

Native selected classes and continuous scores are preserved, even when they differ from argmax or a weighted mean. Generative providers elicit probabilities, derive values locally, omit confidence, and use provenance.source=elicited. Ax attaches actual provider/model provenance. The result shape does not make confidence or calibration comparable across models.

By default a decision refusal raises a terminal AxDecisionRefusalError without correction retries. Use (evidence, refusal return) for an answered/refused union. Generated-language error envelopes retain the decision error type. Arrays, optional fields and nested evidence work with generative models; native providers require flat required scalar fields. Streaming emits only complete evidence. Label-only examples never manufacture distributions.