i4h-workflow

i4h Workflows

Purpose

Orient the user from live repository facts, then hand execution to the narrowest stage skill.

Instructions

1. Run the base-checkout resolver.
2. Read live support and DESIGN.md.
3. Use only current architecture facts in the answer.
4. Use the narrowest stage skill for execution.

Resolve the checkout

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com

Treat this resolver as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.

Inspect before answering

Read ./DESIGN.md for architecture and skills/i4h-workflow/references/repo-map.md for ownership. Discover current support instead of copying a static table:

If discovery fails because setup is incomplete, report that limitation and route to i4h-workflow-setup.

Explain the design

Keep the summary precise:

• A Scene owns the simulated world, assets, embodiment, cameras, randomization, adapters, and reset hooks.
• A Task owns one reusable capability. It reads ctx.scene, writes ctx.act, and never advances the simulator.
• A Workflow selects one Scene, exposes run-mode-specific TaskGraph builders, and owns goal semantics. A run mode answers how that workflow should run; code and CLI use the shorter term mode.
• The Engine schedules graph nodes; the shared SimulationRunner alone resets, steps, renders, records, retries whole episodes, and prints run summaries.
• Online RL is a separate training lifecycle: its trainer owns vectorized stepping and returns a checkpoint to the normal policy Task and SimulationRunner validation path.
• Simulator-compatible exported RSL-RL actors may run as in-process Tasks; incompatible foundation-model policy stacks remain remote.
• Remote policy stacks run out of process and communicate over Zenoh; offline dataset tools remain independent of the simulator.
• Python owns behavior. Manifests carry facts across dependency boundaries.

Do not describe retired environment YAMLs, per-mode runners, or separate policy/Arena launchers.

Route the next action

For Stop all, do not load a stage skill. Run ./stop.sh all from the repository root and report the stopped process count.

Troubleshooting

If discovery fails, verify the resolved checkout and run setup. If a mode is absent, report it as unsupported.

Prerequisites

Require a readable base checkout or network access to clone it.

Limitations

This router does not install, author, simulate, process data, train, or evaluate.

Examples

• What does the i4h workflow include, and where should I start? → inspect live support, summarize DESIGN.md, and recommend one stage skill.

Completion gate

Answer with the live workflow/mode list, a short architecture summary, and one concrete next skill. If the requested workflow or mode is absent from run.sh list, say it is unsupported instead of inventing a command.