agentleFS
DexForceDexForce/EmbodiChain/.agents/skills/add-embodiment-component/SKILL.md

add-embodiment-component

Skill240 starsChanged 11 days agoApache-2.0View on GitHub

Add or update a reusable EmbodiChain embodiment YAML component that owns one simulation robot, its sensor suite, and an optional Task Program skill_profile. Use for configs/components/embodiments, new Task Program-compatible robots, or multi-embodiment task deployment.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 5 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Create the reusable physical and semantic declaration that lets one robot configuration serve many tasks.

    line 8
  2. Very specific:

    Read agent_context/MAP.yaml, then resolve:

    line 32
  3. Very specific:

    If an existing registered robot config can express the request, select it in simulation and add only necessary config overrides.

    line 44
  4. Very specific:

    Keep robot-mounted camera calibration in the robot configuration.

    line 49
  5. Very specific:

    Use logical resource IDs such as primary_manipulator, left, or right.

    line 71
  6. Very specific:

    Put embodiment-calibrated services in skill_profile.runtime_services, such as gripper-specific antipodal grasp generation or control-part …

    line 77
  7. Distinctive:

    Only use service kinds accepted by the current configured decoder.

    line 85
  8. Distinctive:

    verify the combined independent/mounted sensor UIDs, parent links and shapes.

    line 110
  9. Distinctive:

    verify every profile endpoint/control part/capability and command preset.

    line 111
  10. Distinctive:

    run a minimal live robot/sensor smoke test when available.

    line 115

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Add Embodiment Component

Create the reusable physical and semantic declaration that lets one robot configuration serve many tasks.

Establish scope

An embodiment component belongs at:

extensions/embodichain_tasks/configs/components/embodiments/<embodiment_id>.yaml

It owns exactly:

  • embodiment_id;
  • one simulation robot mapping;
  • the independent sensor list (mounted defaults belong to RobotCfg.sensors);
  • optional skill_profile.

It does not own a task, physical scene, Task Program, execution policy, task-local object IDs, or runnable Gym ID. Component files do not use a compatibility version field.

Load current context

Read agent_context/MAP.yaml, then resolve:

  • robot-system;
  • sensor-system; and
  • task-programs when a skill_profile is requested.

Inspect the closest current component under extensions/embodichain_tasks/configs/components/embodiments/ and the intended runnable task deployment.

Reuse or add the robot

If an existing registered robot config can express the request, select it in simulation and add only necessary config overrides. If a new RobotCfg/URDF/control-part implementation is required, invoke $add-robot first. This skill does not duplicate robot registration logic in YAML.

Keep robot-mounted camera calibration in the robot configuration. The component selects that robot and declares additional independent sensors; do not repeat mounted defaults in sensor. Sensor UIDs must be unique across both sources. Gym initializes both sources by default and uses enable_sensor: false to disable both, overriding the robot's standalone initialization switch.

Add skill_profile only when needed

Handwritten and RL deployments may use an embodiment without semantic metadata. A configured Task Program requires skill_profile with:

  • contract_id: the compatibility contract tasks/policies require;
  • profile_id: stable unversioned profile identity;
  • resources: logical task-facing resources;
  • command_presets: named control commands;
  • optional runtime_services.

Each resource declares logical endpoints. Endpoint declarations map to actual control_part names and list only capabilities the selected robot/runtime can provide. Grasp endpoints that require open/grasp commands select a matching command preset.

Use logical resource IDs such as primary_manipulator, left, or right; Task Program integrations bind Semantic Call slots to these IDs. Do not expose task-local names as reusable resources.

Runtime service ownership

Put embodiment-calibrated services in skill_profile.runtime_services, such as gripper-specific antipodal grasp generation or control-part evidence. Put task-specific targets, poses, and overrides in the task integration.

For reusable joint-position constraint evidence, omit object_ids unless the embodiment truly supports only an explicit global set. Omission lets runtime assembly scope evidence to graspable objects in the selected task scene.

Only use service kinds accepted by the current configured decoder. Never serialize a dotted import or callable.

Attach without copying

A runnable deployment selects:

embodiment:
  component: ../../../components/embodiments/<embodiment_id>.yaml

Optional deployment overrides are limited to uid, init_pos, init_rot, and init_qpos. Do not repeat inline robot or sensor fields.

One task can provide multiple thin deployments selecting compatible embodiments. Keep the program embodiment-independent and validate the integration and policy contracts against every variant.

Validation

At minimum:

  1. decode the YAML as a closed embodiment component;
  2. build the selected robot config and verify control parts;
  3. verify the combined independent/mounted sensor UIDs, parent links and shapes;
  4. verify every profile endpoint/control part/capability and command preset;
  5. compose at least one intended task deployment;
  6. for Task Program use, run $add-task-program's deployment inspector;
  7. run a minimal live robot/sensor smoke test when available.

Checklist

  • Component selects exactly one robot and declares only additional sensors
  • Existing robot config was reused, or $add-robot was completed first
  • Profile is omitted when no semantic consumer needs it
  • Contract/profile/resource/preset IDs are stable and unversioned
  • Endpoint control parts exist on the robot
  • Declared capabilities and commands are actually supported
  • Reusable services contain no task-local object IDs or poses
  • At least one intended deployment composes successfully

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.