---

name: pipeline-programming description: "Build, run, batch, debug, and smoke-check Towhee custom pipelines with pipe.input, RuntimePipeline, AutoConfig, and AutoPipes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Towhee pipeline programming

Use this sub-skill when the user needs to define or diagnose a Towhee custom pipeline with pipe.input(...), chained node methods, output(...), RuntimePipeline.__call__, batch(...), debug(...), or flush(). It also covers safe, local-only checks for AutoConfig and network-aware guidance for AutoPipes.pipeline(...).

Fast routing

1. For pipeline node syntax, callable contracts, schemas, RuntimePipeline, batch, flush, AutoConfig, and AutoPipes, use Pipeline API.
2. For profiler/tracer usage, debug result inspection, include/exclude filtering, and batch debug, use Debugging and profiling.
3. For failures involving schema names, missing output columns, debug() flags, Hub/cache/network misses, or optional Hub operator downloads, use Troubleshooting.
4. For a no-network smoke check in the target environment, run pipeline_smoke.py with --verbose when useful.

Scope boundaries

• Stay here for custom pipeline construction, local lambda/callable nodes, node semantics, runtime execution, batch/debug/flush, AutoConfig.*, and AutoPipes.pipeline selection caveats.
• Route operator template creation, Hub operator packaging, ops registration patterns, and CLI operator scaffolding to operator-hub-and-cli.
• Route HTTP/GRPC services, Docker, Triton server startup, towhee server, and deployment packaging to serving-and-triton.
• Route DataCollection display, entity/type wrappers, serializers, and data loader details to data-utilities.
• Route model training, Trainer, and NNOperator.train/setup_trainer workflows to training-and-models.

Default safe workflow

python scripts/pipeline_smoke.py --verbose

If the smoke check fails at import time, fix the active Python environment before attempting Hub, model, Triton, or deployment workflows. If the smoke check passes, prefer lambda/callable pipelines for quick local diagnosis before introducing Hub operators that may download code, models, or optional dependencies.