executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
operator-hub-and-cliOperate Towhee ops wrappers, local operator registration, Hub revisions, and CLI help validation without unsafe downloads or server startup.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when a task needs Towhee operator lookup, local operator development boundaries, Hub operator version selection, towhee init guidance, package entry points, or help-only CLI validation.
PyOperator, and NNOperator boundaries, use references/operator-api.md.towhee, towhee init, towhee server --help, python -m towhee, and package entry points, use references/cli-reference.md.pkg_resources, Hub downloads/cache, accidental server startup, template writes, registry naming, and NN training prerequisites, use references/troubleshooting.md.NNOperator.train() usage to training-and-models.towhee server startup, HTTP/GRPC clients, Triton, Docker, and port management to serving-and-triton.pipe.input(...).map(...).output(...) design to pipeline-programming.towhee init, or live servers as routine validation unless the user explicitly requests those side effects and supplies an isolated target.from towhee import ops, register
from towhee.operator import PyOperator, NNOperator
@register(name='add_operator')
class AddOperator(PyOperator):
def __init__(self, factor):
self.factor = factor
def __call__(self, x):
return x + self.factor
op = ops.add_operator(10) # lazy wrapper; loads when called
assert op(2) == 12
Prefer pinned Hub revisions for reproducibility:
op = ops.some_namespace.some_operator().revision('main')
Use .latest() only when intentionally refreshing a cached Hub operator.