Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
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 wshobson/agents --skill spark-memory-thermal-opsgit clone --depth 1 https://github.com/wshobson/agentsWrote 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/wshobson/agents/spark-memory-thermal-ops)<a href="https://agentmods.dev/skills/wshobson/agents/spark-memory-thermal-ops"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-memory-thermal-ops/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/wshobson/agents/spark-memory-thermal-ops"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-memory-thermal-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00059 | $0.02040 |
| Opus 5 | $0.00030 | $0.01020 |
| Sonnet 5 | $0.00012 | $0.00408 |
| Haiku 4.5 | $0.00006 | $0.00204 |
Grade A, and why
spark-memory-thermal-ops 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark Memory & Thermal Ops
DGX Spark's GB10 chip has one 128GB unified
memory (UMA) pool shared by CPU and GPU, and a
sustained power ceiling well below its rated
figure. Both break discrete-GPU assumptions:
headroom isn't what nvidia-smi reports, and a
run that starts fast will slow down mid-job
with nothing misconfigured. This skill covers
planning memory headroom, working an actual
OOM, and watching thermals across a long job.
For launch-time failure modes (ABI mismatches,
flash-attn, playbook breakage), see
spark-training-gotchas — this skill assumes
the job starts.
Common Issues Quick Reference
| Situation | Do this |
|---|---|
| Planning headroom before launch | Budget against free -g, not nvidia-smi — see UMA Memory Model |
| Job OOMs on unified memory | Work the OOM Ladder in order: flush, then batch/pack, then method downgrade |
| Throughput drops mid-run | Check the power/temp log before assuming a config bug — see Thermal Monitoring |
| Trainer + inference server both wanted | Run one at a time — see Concurrent Workloads |
When to Use This Skill
- Sizing a training run against the 128GB pool before launch — will this model, method, and batch/pack combination fit.
- A run OOMs mid-load or mid-step and the remediation order matters — what to try first, second, third.
- Watching temperature and power during a multi-hour job, deciding whether a slowdown is thermal throttling or something else.
- Planning to run a trainer alongside an inference server (vLLM, Ollama) on the same box.
UMA Memory Model
Spark has no separate GPU VRAM — the GPU and CPU share one 128GB pool. Two consequences:
nvidia-smiandcudaMemGetInfounderreport pressure — or report nothing at all. Both report CUDA-allocator-visible memory, not the pool's actual state — a box can show headroom innvidia-smiand still OOM, because page-cache and mmap'd pages the allocator doesn't see consume the same pool. On some driver/setups, the memory query returns[N/A], [N/A]outright instead of a number — a script grepping for a numeric value there gets nothing, not a misleading undercount (seespark-training-gotchasgotcha G3).
What ships with it
2 files 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.
- 5d ago First seen · 231 lines · 59 tokens per session scan A 7b3e8184ac1c
spark-memory-thermal-ops is a skill published in the GitHub repository wshobson/agents (39,505 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 2,040 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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