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-training-gotchasgit 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-training-gotchas)<a href="https://agentmods.dev/skills/wshobson/agents/spark-training-gotchas"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-training-gotchas/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-training-gotchas"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-training-gotchas.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 | $0.00063 | $0.02241 |
| Opus 5 | $0.00032 | $0.01120 |
| Sonnet 5 | $0.00013 | $0.00448 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
spark-training-gotchas 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark Training Gotchas
DGX Spark's GB10 chip (Grace Blackwell, SM121, 128GB unified memory, aarch64) has ten recurring failure modes across launch, memory, thermals, bandwidth, and precision. Each is named G1–G10 so it can be checked by number — the numbering is load-bearing for tooling that runs these checks. Read this before a long run, not after hour six.
When to Use This Skill
- A training run fails to start, with an import error or a segfault that doesn't point at the real cause.
- A run OOMs while
nvidia-smistill shows headroom. - Throughput degrades partway through a run that started fine.
- Before any multi-hour or multi-epoch job on GB10.
- Wiring two Sparks together, before picking a parallelism strategy.
- Choosing between FP8 and NVFP4 for a Spark-hosted run.
Common Issues Quick Reference
| # | Symptom | Fix |
|---|---|---|
| G1 | undefined symbol / segfault | cu130 wheel or container |
| G2 | flash-attn wrong backend used | skip pip build; monkeypatch on NGC |
| G3 | OOM despite headroom | drop page cache |
| G4 | throughput drop / reboot | expect ~100W sustained cap |
| G5 | memory-bound step slow | budget 180–192 GB/s |
| G6 | cache evicted mid-run | one GPU server at a time |
| G7 | NVFP4 slower than FP8 | stay FP8 unless sm_121a |
| G8 | playbook fails outright | check upstream issues |
| G9 | env breaks after install | use a container |
| G10 | 2-Spark TP hangs | DDP/FSDP only, never TP |
The Ten Gotchas
G1: CUDA 12/13 ABI Mismatch
- SYMPTOM:
ImportError: undefined symbolnaming a CUDA function, or a segfault on the first.cuda()call. - CAUSE: most PyPI wheels link
libcudart.so.12; Spark ships CUDA 13. pip never checks CUDA ABI, so it surfaces only at import or first kernel launch. - CHECK:
references/gotcha-checks.mdG1 — the wheel's CUDA build tag. - FIX: reinstall from
download.pytorch.org/whl/cu130or use a matched container.
G2: flash-attn — Skip the pip Build, Watch Unsloth's Auto-Detect
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 · 201 lines · 63 tokens per session scan A 4ba2e6510c32
spark-training-gotchas is a skill published in the GitHub repository wshobson/agents (39,476 stars, last pushed yesterday), licensed MIT. It adds 63 tokens to every session and 2,241 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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