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name: flow-orchestration description: "Guides LazyLLM pipeline, parallel, branching, looping, binding, graph, and traceable flow orchestration workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LazyLLM Flow Orchestration

Use this sub-skill when a task asks how to compose LazyLLM flows with pipeline, parallel, diverter, switch, ifs, loop, warp, barrier, bind, nested contexts, or traceable graph execution.

Start here when

• The user wants to turn Python functions, model modules, RAG retrievers, or agents into a LazyLLM workflow.
• A flow returns an unexpected tuple/list/dict shape.
• The task mentions _skip_items, _kept_items, _scatter, _concurrent, bind, pipeline() context managers, nested subflows, or conditional/loop execution.
• You need to replace model/RAG/agent nodes with deterministic callables before debugging orchestration.

Files to read

• flow-recipes.md for concise recipes and common shape semantics.
• troubleshooting.md for output-shape, binding, and backend debugging failures.
• scripts/flow_smoke.py for a safe Python-callable flow smoke test.

Safe flow-building sequence

1. Sketch the data shape at each node before adding LazyLLM primitives.
2. Prototype with deterministic Python callables rather than models/providers.
3. Choose the primitive.
   • pipeline: sequential composition.
   • parallel: same input through multiple stages; can return tuples and skip/keep items.
   • diverter: distribute positional/list/tuple/dict inputs to separate functions.
   • switch/ifs: conditional routing.
   • loop: repeated execution with count or stop condition.
   • bind: bind flow inputs or prior stage outputs into callable parameters.
4. Run scripts/flow_smoke.py to confirm the environment and expected semantics.
5. Swap in heavy nodes last and route their setup to model-deployment, RAG, agents-tools, or writer-review.

Verified examples from tests

• pipeline(add_one, add_one)(1) == 3.
• Context manager pipelines can name stages and bind the original input or previous outputs into later stages.
• parallel(add_one, add_one)(1) == (2, 2).
• _skip_items and _kept_items select by index or by named stage, but cannot be provided together.
• switch supports predicate-to-action maps, default cases, conversion functions, and full-input judging.
• ifs supports booleans and callable conditions and propagates real condition exceptions.
• loop supports fixed counts, stop conditions, and runtime limit expansion.

Cross-sub-skill links

• Use model-deployment when a flow node calls OnlineModule, TrainableModule, or ServerModule.
• Use rag-document-processing when a flow node ingests documents, retrieves nodes, or reranks contexts.
• Use agents-tools when a flow node is a tool manager or agent.
• Use writer-review when flow output is a writer artifact or review command plan.

Handoff checklist

When you finish a flow task, provide:

• primitive choice and why,
• input/output shape for each stage,
• minimal deterministic smoke result,
• where model/RAG/agent/writer nodes are configured,
• any concurrency/multiprocessing or side-effect warnings.