get-available-resources
by K-Dense-AIget-available-resources checks CPU, GPU, memory, and disk before heavy scientific or ML workflows. It returns a resource snapshot and practical recommendations for parallel processing, GPU acceleration, or memory-safe approaches, helping agents make better execution choices for workflow automation.
This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. Directory users get a clearly triggerable utility for preflight system checks before compute-heavy scientific work, with enough operational detail to justify installation even though the repo lacks supporting scripts or reference files.
- Explicit trigger condition for scientific tasks that need resource detection before execution
- Operationally clear scope: reports CPU, GPU, memory, and disk, then recommends suitable compute strategies
- Strong implementation signal from substantial SKILL.md content with workflow, constraints, and code examples rather than placeholder text
- No install command, scripts, or companion resources, so adoption depends on reading the skill file rather than following a packaged workflow
- The repository appears focused on one preflight check; it may be less valuable for users wanting broader end-to-end scientific automation
Overview of get-available-resources skill
The get-available-resources skill helps you check the machine before you commit to a heavy scientific or ML workflow. It detects CPU, GPU, memory, and disk resources, then turns that into practical recommendations so you can choose between parallel processing, GPU acceleration, or memory-safe approaches with less guesswork.
This is best for agents and users starting data analysis, model training, large file processing, or any task where runtime and feasibility depend on the environment. The main value of the get-available-resources skill is not just reporting specs, but reducing bad execution choices early.
What it tells you
The skill focuses on the signals that change implementation decisions: how many CPU cores are usable, whether a GPU is present, what memory ceiling to respect, and whether disk space is enough for temporary data, checkpoints, or cached artifacts. That makes the output useful for workflow automation, not just inventory.
When it is a good fit
Use get-available-resources when your prompt depends on system capacity: “Can this run locally?”, “Should I use Dask or plain pandas?”, “Is PyTorch GPU viable here?”, or “How many workers should I request?” It is especially useful when the environment is unknown or variable across hosts.
What makes it different
A generic prompt can guess strategy, but this skill is meant to anchor that guess in current machine conditions. The get-available-resources guide is most valuable when you need a reproducible resource snapshot plus recommendations that can steer later steps.
How to Use get-available-resources skill
Install and locate the skill
Install the get-available-resources install package from the repo path shown in the directory listing, then open scientific-skills/get-available-resources/SKILL.md first. Because this repository does not include helper scripts or extra reference folders, the main skill file is the source of truth.
Give it the right input
The skill works best when you state the task you are about to run and the likely pressure point. For example: “I need to train a tabular model on 40 GB of CSVs” is more useful than “check resources.” That context helps the get-available-resources usage output map capacity to decisions like batching, worker count, or GPU selection.
Read the output as a decision aid
Treat the result as a preflight report. If memory is tight, adjust the pipeline before loading the full dataset. If GPU support is present, confirm the framework/backend you actually use. If disk space is low, plan for smaller intermediates or a different scratch location. The skill is most useful when you act on its recommendations immediately.
Good prompt shape
A strong call usually includes three things: the job, the dataset or model scale, and the preferred stack. For example: “Before running a 12M-row pandas workflow, check resources and recommend whether to use pandas, Polars, or Dask, and how many workers to start with.” That kind of prompt makes the skill output more actionable for Workflow Automation.
get-available-resources skill FAQ
Is this only for scientific computing?
No. It is most relevant for scientific and ML tasks, but any workflow that may hit CPU, GPU, RAM, or disk limits can benefit. If resource constraints can change your implementation plan, the get-available-resources skill is a sensible first step.
Do I need this if I can inspect the machine manually?
Manual checks work, but this skill bundles the check into a reusable workflow and pairs it with recommendations. That matters when you want the same logic applied consistently across different runs or agents.
When should I not use it?
Do not rely on it as a substitute for profiling. It tells you what is available, not what your workload will actually consume. If your task is tiny, fixed, or already benchmarked, the get-available-resources guide may add little value.
Is it beginner-friendly?
Yes, if you can describe your task in plain language. The main learning curve is interpreting the recommendations in relation to your stack, especially when choosing between CPU, GPU, or out-of-core approaches.
How to Improve get-available-resources skill
State the workload, not just the goal
Better inputs describe scale and shape: number of rows, file size, model type, expected peak memory, or whether the task is embarrassingly parallel. “Process a 120 GB parquet dataset” is much better than “analyze my data,” because the skill can frame recommendations around the real bottleneck.
Name the stack you plan to use
If you expect to use PyTorch, JAX, joblib, multiprocessing, Dask, or Zarr, say so. The get-available-resources output becomes more useful when it can recommend a compatible execution path instead of a generic “use GPU” answer that may not fit your code.
Watch for common failure modes
The most common mistake is treating “available” as “safe to use at full capacity.” Leave headroom for the OS, the notebook kernel, model overhead, and temporary files. Another mistake is ignoring disk when the job creates checkpoints, caches, or intermediate arrays. Those constraints matter as much as RAM.
Iterate after the first check
If the first result shows borderline resources, refine the plan before running the full workload. Reduce batch size, limit workers, switch to chunked processing, or choose a smaller model. Use the get-available-resources skill again after major environment changes so the next decision is based on current conditions, not assumptions.