ax-rust-typesafe

Typesafe / Jev For Rust

This skill helps an agent write Rust 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: Rust.
• 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.

Core Pattern

let mut model = ai("typesafe", json!({"api_key": api_key, "trueThreshold": 0.9}))?;
let decision = ax("ticket:string -> urgent:boolean(true \"Core task blocked\", false \"Routine request\"), team:class \"support, billing, engineering\"")?.forward(&mut model, json!({"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.