菜单
Use when writing C++ code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ax-llm/ax --skill ax-cpp-flow命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | ax-cpp-flow |
| description | Use when writing C++ code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components. |
| version | 23.0.14 |
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.
axllm.API.md and axir-api.json.axir-capabilities.json.examples/.javascript-quickjs, python-pyodide.auto draft = axllm::ax("topicText:string -> draftText:string");
auto wf = axllm::flow(axllm::object({{, }}))
.(, draft, axllm::({
{, axllm::({})},
{, axllm::({, })}
}))
.(axllm::({{, }}));
Build each flow node from its own input/output contract.
auto classifier = axllm::ax("requestText:string -> route:class \"support, sales, engineering\"");
auto responder = axllm::ax("requestText:string, route:string -> responseText:string");
Declare reads and writes so the responder waits for the typed route.
auto branch_flow = axllm::flow(axllm::object({{"id", "docs.branchFlow"}}))
.execute("classifier", classifier, axllm::object({{"reads", axllm::array({"requestText"})}, {"writes", axllm::array({"classifierResult", "route"})}}))
.execute("responder", responder, axllm::object({{"reads", axllm::array({"requestText", "route"})}, {"writes", axllm::array({"responderResult", "responseText"})}}))
.returns(axllm::object({{"route", "route"}, {"responseText", "responseText"}}));
Independent reads let research and audience analysis share one planner group.
auto parallel_flow = axllm::flow(axllm::object({{"id", "docs.parallelFlow"}}))
.execute("research", research, axllm::object({{"reads", axllm::array({"topicText"})}, {"writes", axllm::array({"researchResult", "factList"})}}))
.execute("audience", audience, axllm::object({{"reads", axllm::array({"topicText"})}, {"writes", axllm::array({"audienceResult", "audienceAngle"})}}))
.execute("join", join, axllm::object({{"reads", axllm::array({"factList", "audienceAngle"})}, {"writes", axllm::array({"joinResult", "briefText"})}}))
.returns(axllm::object({{"briefText", "briefText"}}));
A linear refinement pipeline makes each dependency explicit.
auto refine_flow = axllm::flow(axllm::object({{"id", "docs.refineFlow"}}))
.execute("draft", draft, axllm::object({{"reads", axllm::array({"topicText"})}, {"writes", axllm::array({"draftResult", "draftText"})}}))
.execute("critique", critique, axllm::object({{"reads", axllm::array({"draftText"})}, {"writes", axllm::array({"critiqueResult", "critiqueText"})}}))
.execute("revise", revise, axllm::object({{"reads", axllm::array({"draftText", "critiqueText"})}, {"writes", axllm::array({"reviseResult", "revisedText"})}}))
.returns(axllm::object({{"revisedText", "revisedText"}}));
Forward accepts the provider client and public inputs.
auto output = parallel_flow.forward(
client,
axllm::object({{"topicText", "Typed LLM workflows"}}));
Start from the complete programs under examples/, then browse the larger gallery at https://axllm.dev/cpp/subsystems/flow/.
axllm::flow, axllm::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.