Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dralkh/seerai --skill get-available-resourcesgit clone --depth 1 https://github.com/dralkh/seeraiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dralkh/seerai/get-available-resources)<a href="https://agentmods.dev/skills/dralkh/seerai/get-available-resources"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/get-available-resources/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dralkh/seerai/get-available-resources"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/get-available-resources.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.02309 |
| Opus 5 | $0.00057 | $0.01154 |
| Sonnet 5 | $0.00023 | $0.00462 |
| Haiku 4.5 | $0.00012 | $0.00231 |
Grade A, and why
get-available-resources scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to get-available-resources — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Get Available Resources
Overview
Detect available computational resources and generate strategic recommendations for scientific computing tasks. This skill automatically identifies CPU capabilities, GPU availability (NVIDIA CUDA, AMD ROCm, Apple Silicon Metal), memory constraints, and disk space to help make informed decisions about computational approaches.
When to Use This Skill
Use this skill proactively before any computationally intensive task:
- Before data analysis: Determine if datasets can be loaded into memory or require out-of-core processing
- Before model training: Check if GPU acceleration is available and which backend to use
- Before parallel processing: Identify optimal number of workers for joblib, multiprocessing, or Dask
- Before large file operations: Verify sufficient disk space and appropriate storage strategies
- At project initialization: Understand baseline capabilities for making architectural decisions
Example scenarios:
- "Help me analyze this 50GB genomics dataset" → Use this skill first to determine if Dask/Zarr are needed
- "Train a neural network on this data" → Use this skill to detect available GPUs and backends
- "Process 10,000 files in parallel" → Use this skill to determine optimal worker count
- "Run a computationally intensive simulation" → Use this skill to understand resource constraints
How This Skill Works
Resource Detection
The skill runs scripts/detect_resources.py to automatically detect:
-
CPU Information
- Physical and logical core counts
- Processor architecture and model
- CPU frequency information
-
GPU Information
- NVIDIA GPUs: Detects via nvidia-smi, reports VRAM, driver version, compute capability
- AMD GPUs: Detects via rocm-smi
- Apple Silicon: Detects M1/M2/M3/M4 chips with Metal support and unified memory
-
Memory Information
- Total and available RAM
- Current memory usage percentage
- Swap space availability
-
Disk Space Information
- Total and available disk space for working directory
- Current usage percentage
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 275 lines · 115 tokens per session scan A 52157b0bca33
get-available-resources is a skill published in the GitHub repository dralkh/seerai (77 stars, last pushed 2mo ago), licensed MIT. It adds 115 tokens to every session and 2,309 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to get-available-resources, differing in 4 lines, and is treated as a copy.
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