get-available-resources

name	get-available-resources
description	Use at the start of computationally intensive local tasks to detect CPU, memory, disk, and optional accelerator availability before planning execution.

Get Available Resources

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/get-available-resources/run_get_available_resources.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 before local work that may be expensive, memory-sensitive, or parallelized, such as document conversion batches, graph enumeration, SageMath runs, OCR, local parsing, or large file rearrangement.

Workflow

1. Decide whether the task is heavy enough to justify a preflight. Skip this skill for trivial commands.
2. Prefer an existing local resource checker when the installed agent provides one. Otherwise inspect resources with portable system commands or Python.
3. Record the result in a small planning note or .agent_resources.json in the current workspace when the task will continue for multiple steps.
4. Use the result to choose batch size, parallelism, memory strategy, and whether to route the task to SageMath, WSL, remote compute, or a smaller local run.

Minimum Checks

• CPU count and rough CPU model.
• Available memory.
• Free disk space in the working directory.
• GPU or accelerator availability only when relevant and detectable.
• Whether the workload should be split, sampled first, or routed elsewhere.

Output Shape

For a visible preflight, report:

• resources inspected
• detected limits
• recommended execution strategy
• confidence and any missing probes

Guardrails

• Do not spend more time on resource detection than the task warrants.
• Do not assume GPU or SageMath availability without checking.
• On Windows, consider WSL-backed tools separately from native Windows tools.
• Treat remote compute credentials and provider configuration as external; do not inspect or print secrets.

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.