---

name: spaces-data description: "Design Gymnasium action and observation spaces, validate samples, flatten/unflatten data, serialize JSONable batches, and debug dtype/shape mismatches." disable-model-invocation: true metadata: disco-role: operating license: MIT

Gymnasium Spaces and Data Contracts

Use this sub-skill when a task involves choosing Gymnasium action or observation spaces, validating samples with contains, seeding or sampling spaces, flattening nested data for model inputs, unflattening model vectors back to structured observations/actions, or serializing space samples.

Start Here

• For constructor choices and data contracts, use references/space-design.md before modifying an environment's action_space or observation_space.
• For flatdim, flatten_space, flatten, unflatten, to_jsonable, and from_jsonable, use references/flattening-and-json.md.
• For Box.contains surprises, integer starts, nested flatten shape mismatches, dynamic spaces, and wrapper space updates, use references/troubleshooting.md.
• To smoke-test the local Gymnasium install and demonstrate the core contract, run python sub-skills/spaces-data/scripts/space_contract_smoke.py --help and then python sub-skills/spaces-data/scripts/space_contract_smoke.py from the root Gymnasium skill directory.

Scope Boundaries

• Use ../environment-api/SKILL.md for custom Env lifecycle, reset/step, registration, check_env, and seeding an environment through reset(seed=...).
• Use ../wrappers-recording/SKILL.md when a wrapper changes observation_space, action_space, observations, actions, rewards, rendering, or recording behavior.
• Use ../vectorization/SKILL.md for batched/vector spaces, gym.vector utilities, single_observation_space, single_action_space, and vector wrapper space transforms.

Minimum Correct Patterns

import numpy as np
from gymnasium import spaces

observation_space = spaces.Dict({
    "position": spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=np.float32),
    "inventory": spaces.Tuple((spaces.Discrete(5), spaces.MultiBinary(3))),
})

observation = observation_space.sample()
assert observation_space.contains(observation)

from gymnasium.spaces import utils as space_utils

flat = space_utils.flatten(observation_space, observation)
restored = space_utils.unflatten(observation_space, flat)
assert observation_space.contains(restored)

Keep runtime guidance self-contained. Do not depend on Gymnasium source, tests, examples, or documentation paths at runtime.