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
environments-and-vectorizationCreate and validate Gymnasium custom environments for Stable-Baselines3, then wrap them safely with Monitor, DummyVecEnv/SubprocVecEnv, VecNormalize, VecCheckNan, and frame/dict/image vector wrappers.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is about Stable-Baselines3 environment compatibility rather than algorithm selection: custom Gymnasium env APIs, check_env, vectorized env construction, VecEnv API semantics, Monitor logging, normalization, NaN checks, frame stacking, dict/image observations, and multiprocessing caveats.
check_env failure: use references/custom-envs.md for the required reset()/step() API, spaces, image/dict observations, unsupported spaces, and the validation checklist.make_vec_env, DummyVecEnv, SubprocVecEnv, automatic resets, terminal_observation, seeding/options, VecNormalize, VecCheckNan, VecFrameStack, and dict/image wrappers.python sub-skills/environments-and-vectorization/scripts/check_custom_env.py --mode valid or --mode invalid from a copied skill tree that has Stable-Baselines3 installed.training-and-algorithms.VecNormalize persistence during evaluation, or rollout metrics, route to evaluation-and-persistence.MultiInputPolicy internals, or normalize_images=False policy details, route to policies-and-customization after confirming the env observation routing here.