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name: extension-patterns description: "Add and inspect Habitat-Lab registry extensions for tasks, sensors, measures, actions, datasets, simulators, and baselines components." disable-model-invocation: true metadata: disco-role: operating license: MIT

Habitat-Lab Extension Patterns

Use this sub-skill when you need to add, inspect, debug, or review custom Habitat-Lab extension classes that are discovered through habitat.registry or baseline_registry. It covers registration and config alignment; it does not run environments or train agents.

Fast Route

• For exact decorator/getter names, ownership boundaries, and config-name alignment, read references/registry-api.md.
• For recipes for sensors, measures, actions, tasks, datasets, simulators, policies, trainers, storage, updaters, and observation transforms, read references/extension-patterns.md.
• For failure diagnosis around missing keys, duplicate registrations, action spaces, measure dependencies, and transform shapes, read references/troubleshooting.md.
• To safely inspect currently importable registry keys without creating a simulator or Env, run scripts/inspect_registry_extensions.py from a checkout or installed environment.

Working Rules

1. Define the extension class in an importable Python module and decorate it with the matching registry decorator.
2. Ensure the module is imported before config construction or before a factory calls registry.get_* or baseline_registry.get_*.
3. Align every config type value with the registry key, not necessarily with the config dictionary key or emitted observation/action UUID.
4. Keep runtime tests narrow: registry lookup, config-node creation, observation/action space compatibility, and shape contracts can be checked without full simulator assets.
5. Escalate full Env execution, dataset loading, and training/evaluation to the task/env and baselines sub-skills.

Common Extension Flow

1. Pick the registry surface: habitat.registry for lab runtime components, baseline_registry for training/inference components.
2. Pick a stable registry key with @...register_*(name="...") when the public config name should differ from the class name.
3. Create or update the matching structured config class so its type field equals that registry key.
4. Add the config node under the correct collection, such as habitat.task.lab_sensors, habitat.task.measurements, habitat.task.actions, habitat.dataset, habitat.simulator, habitat_baselines.trainer_name, habitat_baselines.rl.policy.name, or habitat_baselines.rl.agent.type.
5. Import the module containing the decorator before the lookup site runs.
6. Probe with registry lookups and shape/space checks before attempting asset-backed execution.

Safe Probe

From this sub-skill directory, run the bundled probe to list registered keys:

python scripts/inspect_registry_extensions.py --all

Useful narrow probes:

python scripts/inspect_registry_extensions.py --category sensor --category measure
python scripts/inspect_registry_extensions.py --import-module my_project.habitat_extensions --category task_action --category policy
python scripts/inspect_registry_extensions.py --key measure:MyMeasure --key trainer:ppo

The script imports registries and optional extension modules, then prints mapping keys. It does not instantiate Env, datasets, simulators, trainers, or policies.