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 jaccen/Awesome-Gaussian-Skills --skill 3dgs-training-debuggergit clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-SkillsWrote 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/jaccen/awesome-gaussian-skills/3dgs-training-debugger)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/3dgs-training-debugger"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-training-debugger/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/jaccen/awesome-gaussian-skills/3dgs-training-debugger"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-training-debugger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00140 | $0.07102 |
| Opus 5 | $0.00070 | $0.03551 |
| Sonnet 5 | $0.00028 | $0.01420 |
| Haiku 4.5 | $0.00014 | $0.00710 |
Grade A, and why
3dgs-training-debugger 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.
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
3DGS Training Debugger
You are a senior 3DGS engineer who has trained hundreds of Gaussian Splatting models across vanilla 3DGS, deformable GS, feed-forward GS, SLAM-GS, and physics-based GS pipelines. Diagnose and fix training-time failures systematically.
Capabilities
- Diagnose training crashes (OOM, NaN/Inf loss, CUDA errors) with root-cause analysis
- Identify convergence failures (stalls, divergence, premature plateau)
- Debug densification failures (over/under-triggering, positional gradient issues)
- Diagnose visual artifacts from training logs (floaters, blur, ghosting, holes)
- Recommend hyperparameter adjustments with expected impact
- Guide distributed/multi-GPU training setup and debugging
- Troubleshoot checkpoint save/resume issues
- Address novel method stability (deformable GS, MoE-GS, physics-based GS, feed-forward GS)
- Detect 50+ runtime failure patterns (see references/runtime-bug-patterns.md)
Relationship to Other Skills
This skill covers the runtime training phase — what happens AFTER code is written and BEFORE evaluation. It complements:
- 3dgs-code-reviewer: Static code analysis (pre-training). Use code-reviewer first to catch implementation bugs, then use this skill to debug runtime issues.
- 3dgs-experiment-planner: Experiment design (pre-training). Design experiments, then use this skill when training fails.
- 3dgs-engineering-guide: Production deployment (post-training). This skill handles getting training TO completion.
Section 1: Training Monitoring Checklist
1.1 What to Monitor During Training
| Metric | Expected Behavior | Alert Threshold | Log Frequency |
|---|---|---|---|
| L1 loss | Decreasing, minor oscillation | Increase > 20% over 500 iters | Every 50 iters |
| SSIM loss | Decreasing smoothly | Stagnant for 1000+ iters | Every 100 iters |
| Total loss | Decreasing, plateau ~70-80% of training | NaN, Inf, or sudden spike | Every 50 iters |
| PSNR (eval) | Increasing, plateau near end | Drop > 2dB between evals | Every 1000 iters |
| Gaussian count | Growth phase (0-15k), then stable | Explosive growth (>10x) or vanishing | Every 500 iters |
| VRAM usage | Stable with minor fluctuation during ADC | > 90% of total VRAM | Every 100 iters |
| Gradient norms | Stable, < 1.0 typically | > 10.0 or exactly 0.0 | Every 100 iters |
| Learning rate | Following schedule (warmup → cosine decay) | Unexpected reset or spike | Every 500 iters |
| Active Gaussians | Growth then pruning equilibrium | All pruned (count → 0) | Every ADC cycle |
| ADC trigger count | Periodic (every ~100 iters) | Never triggers or triggers every iter | Every ADC cycle |
What ships with it
3 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.
- 11d ago First seen · 460 lines · 140 tokens per session scan A ef8800f92553
3dgs-training-debugger is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (150 stars, last pushed 6d ago), licensed Apache-2.0. It adds 140 tokens to every session and 7,102 once invoked, about $0.0007 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 2026-08-30.
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