Add Task Program

Build a provider-independent task flow and bind it to trusted physical components without leaking robot or simulator details into the program.

Route from the prompt

Select one or more modes and read only their references:

Prompt asks for	Mode	Reference
Task flow, nodes, targets, calls, segments, repeats, parallel blocks, Python TaskProgramCfg	Author program	references/program-authoring.md
Generate/fill integration.yaml, map scene UIDs, choose profiles/services, fix contract mismatch	Compose integration	references/integration-composition.md
Add task.<embodiment>.yaml, reuse env/embodiment/policy components, add another embodiment	Attach deployment	references/deployment.md
Validate, inspect, diagnose, migrate, or repair an existing Task Program deployment	Validate/repair	references/validation.md

For a complete new Task Program expert task, use the modes in this order:

1. author program;
2. compose integration;
3. attach deployment;
4. validate.

Use $add-task-env for a missing physical task/environment baseline. Use $add-embodiment-component for a missing reusable robot/sensor/skill profile. Use $add-semantic-call only when the program needs a Semantic Call that the selected catalog cannot discover.

Load current context

Read agent_context/MAP.yaml, resolve task-programs, and verify the matched source-of-truth files before editing. Also load env-framework when changing a Gym deployment.

Inspect the selected:

• physical environment component;
• embodiment component and optional skill_profile;
• execution policy;
• nearest official Task Program example; and
• tests covering the changed configuration boundary.

Do not read the human-facing Sphinx docs unless the user asks for them.

Preserve ownership boundaries

program.yaml
  task flow, targets, post-policies, validators

integration.yaml
  integration identity and required contracts
  semantic scene_binding
  task semantic defaults/options/effect monitors
  task-specific allowlisted runtime services

env.yaml
  physical scene and ordinary Gym environment values

components/embodiments/*.yaml
  simulation robot, sensors, optional reusable skill_profile

components/execution_policies/*.yaml
  motion, tracking, recovery, runner, effect assurance

task.<embodiment>.yaml
  runnable ID and component selections

Never put robot IDs or an integration selection into serialized program.yaml. Never put Task Program metadata in env.yaml or a physical scene component. Do not create scene.yaml; semantic scene data is nested in integration.yaml.scene_binding.

Components use closed fields and do not carry compatibility version values. Do not serialize dotted imports, arbitrary callables, or executable payloads.

Integration generation principles

When generating an integration, derive values from selected components:

• map logical scene entities to native UIDs that actually exist in the physical environment;
• take the embodiment contract, resources, endpoints, capabilities, command presets, and reusable services from skill_profile;
• take the execution-policy contract and runtime preset from the selected policy;
• declare only task-specific semantic defaults, action options, effect monitors, scene affordances, and services in the integration.

Do not invent a resource ID, endpoint, capability, runtime service kind, or Semantic Call. If the current catalog/decoder cannot represent the request, stop config generation at that boundary and route to the owning extension skill.

One task program may be deployed against multiple embodiments only when the integration's required contract is satisfied by each selected profile and policy. Deployment overrides do not repair a semantic contract mismatch.

Validation ladder

Use the smallest level that proves the requested work:

1. strict file decode and closed-field validation;
2. component resolution and physical UID validation;
3. scene/embodiment/policy contract composition;
4. catalog preflight and Semantic Call lowering;
5. focused unit/integration tests;
6. live environment execution with effect assurance and validators.

Run the bundled read-only inspector for complete configured deployments:

python .agents/skills/add-task-program/scripts/inspect_deployment.py \
  embodichain_tasks/configs/tasks/<category_path>/<task_name>/task.<embodiment>.yaml

This command performs levels 1-4 without constructing a simulator. It does not prove physical success.

Completion checklist

• Prompt was routed to the required mode reference(s)
• Program remains provider- and embodiment-independent
• Integration values were derived from current physical/profile/policy data
• Every scene_binding root maps to a declared physical UID
• Contract IDs agree across integration, embodiment, and policy
• Runtime services and registered calls are allowlisted and fully covered
• Deployment paths resolve relative to the runnable task config
• Static inspector passes
• Focused tests pass
• Physical qualification was run or explicitly reported as not run