---

name: wrappers-recording description: "Apply, inspect, author, and troubleshoot Gymnasium single-environment wrappers, recording helpers, and rendering helpers." disable-model-invocation: true metadata: disco-role: operating license: MIT

Gymnasium Wrappers and Recording

Use this sub-skill when a task needs single-environment Gymnasium wrappers: applying wrapper chains, inspecting wrapped environments, writing custom wrappers, recording episode statistics or videos, and diagnosing wrapper or render-mode failures.

Start Here

1. Build the base environment with gymnasium.make(...); most made environments already include TimeLimit<OrderEnforcing<PassiveEnvChecker<...>>> unless the spec/options disable parts of that stack.
2. Add wrappers outside-in: env = Wrapper(env, ...); the last wrapper assigned is the first object the agent interacts with.
3. Inspect env for the full chain, env.env for the next inner layer, and env.unwrapped for the original base environment.
4. After any wrapper that changes observations or actions, verify env.observation_space, env.action_space, reset(), and one step() agree.
5. Use RecordEpisodeStatistics for lightweight metrics; add RecordVideo only when the environment was created with image-returning rendering such as render_mode="rgb_array" and moviepy is installed.

Route by Task

Task	Read
Choose or order built-in wrappers	references/wrapper-catalog.md
Record episode returns, lengths, videos, or render frames	references/recording-and-rendering.md
Write Wrapper, ObservationWrapper, ActionWrapper, or RewardWrapper subclasses	references/custom-wrappers.md
Diagnose wrapper order, changed spaces, render/video, image wrapper, Atari, or array conversion errors	references/troubleshooting.md
Prove a local wrapper chain works	scripts/wrapper_smoke.py

Common Patterns

import gymnasium as gym
from gymnasium.wrappers import FlattenObservation, RecordEpisodeStatistics, TimeLimit

env = gym.make("CartPole-v1")
env = TimeLimit(env, max_episode_steps=200)
env = FlattenObservation(env)
env = RecordEpisodeStatistics(env)
obs, info = env.reset(seed=123)
obs, reward, terminated, truncated, info = env.step(env.action_space.sample())

import gymnasium as gym
from gymnasium.wrappers import RecordVideo

env = gym.make("CartPole-v1", render_mode="rgb_array")
env = RecordVideo(env, video_folder="videos", episode_trigger=lambda episode: True)

Boundaries

• For vector environments and gymnasium.wrappers.vector, use ../vectorization/SKILL.md.
• For detailed Space constructor, flattening, dtype, and contains guidance, use ../spaces-data/SKILL.md.
• For environment registration, check_env, and custom Env lifecycle, use ../environment-api/SKILL.md.
• For built-in environment family extras such as Box2D, MuJoCo, Atari/ALE IDs, or Toy Text action masks, use ../builtin-envs/SKILL.md.