modal-research-compute

name	modal-research-compute
description	Use when a research or engineering task needs automatic heavy-compute routing through the local broker for Modal-backed remote CPU, high-memory CPU, or GPU execution.
metadata	{"short-description":"Route heavy compute to Modal or GitHub Actions through the local broker"}

Modal Research Compute

Windows Runtime Commands

On native Windows, use the managed Windows runner and the native runtime command target. Set $runtime to the installed runtime root. Multi-agent installs usually use %LOCALAPPDATA%\ai-agents-skills\runtime. Then run:

$runtime = if ($env:AAS_RUNTIME_ROOT) { $env:AAS_RUNTIME_ROOT } else { "$env:LOCALAPPDATA\ai-agents-skills\runtime" }
& "$runtime\run_skill.bat" "skills/modal-research-compute/run_modal_research_compute.bat" <args>

POSIX examples below use run_skill.sh and .sh command targets; use the Windows command target above on native Windows.

Use this skill when the task is about:

• exhaustive search
• object enumeration
• counterexample hunting
• large parameter sweeps
• remote execution of generated experiment code
• GPU-suitable document, embedding, reranking, or tensor workloads

This skill is the integration layer for the local research_compute broker.

When to prefer this skill

• the user wants Modal involved automatically
• the local machine is CPU, memory, disk, or GPU constrained for the requested workload
• the workload is long-running enough that remote execution is a better fit

Core workflow

1. If local resources matter, run get-available-resources.
2. Build a broker manifest JSON for the task.
3. Run broker plan.
4. If the plan stays within policy, run broker submit.
5. Use wait and fetch to retrieve results and logs back to local storage.

Runtime commands

Linux (resolve the installed runtime root for the current agent, then call run_skill.sh):

runtime="${AAS_RUNTIME_ROOT:-$HOME/.local/share/ai-agents-skills/runtime}"
run() { bash "$runtime/run_skill.sh" skills/modal-research-compute/run_modal_research_compute.sh "$@"; }

run bootstrap                          # one-time: generate config if absent, authenticate gh, check deps, doctor
run doctor                             # broker + config + Modal + (if enabled) GitHub Actions readiness
run plan   /path/to/job.json
run submit /path/to/job.json
run wait   <job_id>
run fetch  <job_id> --dest /path/to/output

On targets that install a local skill wrapper, that wrapper should forward to the same runtime command target.

skills/modal-research-compute/run_modal_research_compute.sh doctor

Windows:

$runtime = if ($env:AAS_RUNTIME_ROOT) { $env:AAS_RUNTIME_ROOT } else { "$env:LOCALAPPDATA\ai-agents-skills\runtime" }
& "$runtime\run_skill.bat" `
  "skills\modal-research-compute\run_modal_research_compute.bat" `
  doctor

Operational notes

• The broker is the decision boundary. Do not call Modal directly from the normal Codex flow when the broker can handle the task.
• CPU-heavy combinatorial workloads should default to remote CPU or high-memory CPU, not GPU.
• GPU use should be explicit in the manifest or clearly justified by the workload.
• doctor and plan work without a deployed Modal app. submit, wait, fetch, and deploy need the host to be Modal-ready.
• Linux hosts become Modal-ready after python3 -m pip install --user --upgrade modal and modal token set or modal token new.
• Windows hosts should install modal into the selected Python environment and ensure modal.exe is on PATH so broker deploy can find it.
• Broker state persists under the runtime memories tree, while fetched outputs materialize under the caller workspace by default.
• One-time per machine, run bootstrap: it generates research-compute.toml from the example if absent (never overwriting an existing one), authenticates gh, checks deps, and runs doctor. Use this to set up a host that does not have the full system installer.
• GitHub Actions ToS compliance: the broker's gha lane runs only inside a private research repo, executes that repo's own committed experiment code (parameters are data, never executed), is budget-gated, and is the last automatic backend after local and Modal — never a general compute pool. Configure it under [gha] in research-compute.toml.

Recommended templates

When this skill is involved, consider these workflow templates (install via the workflow-templates artifact profile, or --with-deps to pull backing skills):

• autonomous-research-loop-runbook -- Bounded autonomous research-loop runbook with four stop conditions, single-path solving, mandatory cross-agent verification, fresh-agent backtracking, and Modal/GitHub Actions credit-gated heavy-compute offload.
• engineering-delivery-loop-runbook -- Bounded build-and-deliver loop runbook: single-path implementation with seen-to-fail proof, cross-agent diff verification, behavior-preserving cleanup, and credit-gated heavy-compute offload.