Use when writing Rust code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
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.
axllm.API.md and axir-api.json.axir-capabilities.json.examples/.javascript-quickjs.let draft = axllm::ax("topicText:string -> draftText:string")?;
let wf = axllm::flow("docs.coreFlow")
.execute_with_options(
"draft",
draft,
&json!({"reads": ["topicText"], "writes": ["draftResult", "draftText"]}),
)
.returns(json!({"draftText": "draftText"}));Build each flow node from its own input/output contract.
let classifier = axllm::ax("requestText:string -> route:class \"support, sales, engineering\"")?;
let responder = axllm::ax("requestText:string, route:string -> responseText:string")?;Declare reads and writes so the responder waits for the typed route.
let mut branch_flow = axllm::flow("docs.branchFlow")
.execute_with_options("classifier", classifier, &json!({"reads": ["requestText"], "writes": ["classifierResult", "route"]}))
.execute_with_options("responder", responder, &json!({"reads": ["requestText", "route"], "writes": ["responderResult", "responseText"]}))
.returns(json!({"route": "route", "responseText": "responseText"}));Independent reads let research and audience analysis share one planner group.
let mut parallel_flow = axllm::flow("docs.parallelFlow")
.execute_with_options("research", research, &json!({"reads": ["topicText"], "writes": ["researchResult", "factList"]}))
.execute_with_options("audience", audience, &json!({"reads": ["topicText"], "writes": ["audienceResult", "audienceAngle"]}))
.execute_with_options("join", join, &json!({"reads": ["factList", "audienceAngle"], "writes": ["joinResult", "briefText"]}))
.returns(json!({"briefText": "briefText"}));A linear refinement pipeline makes each dependency explicit.
let mut refine_flow = axllm::flow("docs.refineFlow")
.execute_with_options("draft", draft, &json!({"reads": ["topicText"], "writes": ["draftResult", "draftText"]}))
.execute_with_options("critique", critique, &json!({"reads": ["draftText"], "writes": ["critiqueResult", "critiqueText"]}))
.execute_with_options("revise", revise, &json!({"reads": ["draftText", "critiqueText"], "writes": ["reviseResult", "revisedText"]}))
.returns(json!({"revisedText": "revisedText"}));Forward accepts the mutable provider client and public inputs.
let output = parallel_flow.forward(
&mut client,
json!({"topicText": "Typed LLM workflows"}),
)?;Start from the complete programs under examples/, then browse the larger gallery at https://axllm.dev/rust/subsystems/flow/.
flow, AxFlowprovider-api examples only when the user explicitly has provider credentials available.no-key examples for deterministic local checks and provider request mapping.tools/*/skills/ into user packages.If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.