| 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
- Decide whether the task is heavy enough to justify a preflight. Skip this
skill for trivial commands.
- Prefer an existing local resource checker when the installed agent provides
one. Otherwise inspect resources with portable system commands or Python.
- Record the result in a small planning note or
.agent_resources.json in the
current workspace when the task will continue for multiple steps.
- 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.
