القائمة
Use when writing Python code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/ax-llm/ax --skill ax-python-flowيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Use when writing C++ code with `axllm` for deciding between context maps, trajectory context policy, offline optimization (ACE/GEPA), and memory recall for long-context agents.
Use when writing C++ code with `axllm` for agent memory, recall callbacks, dynamic skill discovery, loaded-skill state, and used-skill tracking.
Use when writing C++ code with `axllm` for agent tracing, centralized and multi-tenant usage accounting, action logs, runtime diagnostics, replay, and production debugging.
| name | ax-python-flow |
| description | Use when writing Python 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 Python 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.from axllm import ax, flow
draft = ax("topicText:string -> draftText:string")
wf = (
flow({"id": "docs.coreFlow"})
.execute(, draft, {: [], : [, ]})
.returns({: })
)
Build each flow node from its own input/output contract.
classifier = ax('requestText:string -> route:class "support, sales, engineering"')
responder = ax("requestText:string, route:string -> responseText:string")
Declare reads and writes so the responder waits for the typed route.
branch_flow = (
flow({"id": "docs.branchFlow"})
.execute("classifier", classifier, {"reads": ["requestText"], "writes": ["classifierResult", "route"]})
.execute("responder", responder, {"reads": ["requestText", "route"], "writes": ["responderResult", "responseText"]})
.returns({"route": "route", "responseText": "responseText"})
)
Independent reads let research and audience analysis share one planner group.
parallel_flow = (
flow({"id": "docs.parallelFlow"})
.execute("research", research, {"reads": ["topicText"], "writes": ["researchResult", "factList"]})
.execute("audience", audience, {"reads": ["topicText"], "writes": ["audienceResult", "audienceAngle"]})
.execute("join", join, {"reads": ["factList", "audienceAngle"], "writes": ["joinResult", "briefText"]})
.returns({"briefText": "briefText"})
)
A linear refinement pipeline makes each dependency explicit.
refine_flow = (
flow({"id": "docs.refineFlow"})
.execute("draft", draft, {"reads": ["topicText"], "writes": ["draftResult", "draftText"]})
.execute("critique", critique, {"reads": ["draftText"], "writes": ["critiqueResult", "critiqueText"]})
.execute("revise", revise, {"reads": ["draftText", "critiqueText"], "writes": ["reviseResult", "revisedText"]})
.returns({"revisedText": "revisedText"})
)
Forward accepts the provider client and the public flow inputs.
output = parallel_flow.forward(client, {"topicText": "Typed LLM workflows"})
Start from the complete programs under examples/, then browse the larger gallery at https://axllm.dev/python/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.