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
framework-compatibilityRoute TensorFlow 2, legacy TensorFlow 1 with Tensorpack, Keras/Keras-MXNet, and Chainer requests with explicit prerequisites, verification limits, and safe CPU fallbacks.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when a request names TensorFlow 2, TensorFlow 1/Tensorpack, Keras or Keras-MXNet, Chainer, a framework-specific model provider, or a framework-specific training/evaluation/demo command.
pretrained=False, an empty checkpoint argument, one small synthetic
input, and --num-gpus=0 for diagnostics. A pretrained flag can download
weights and is not an offline smoke test.python <entrypoint>.py --help only to inspect a CLI. Do not start
training, evaluate a downloaded dataset, or execute conversion tests while
diagnosing availability.For exact provider names, prepare_model signatures, entrypoint flags, and
failure recovery, use the bundled references rather than expanding this router.