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 yeaight7/agent-powerups --skill training-pipeline-debugginggit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/training-pipeline-debugging)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/training-pipeline-debugging"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/training-pipeline-debugging/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/yeaight7/agent-powerups/training-pipeline-debugging"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/training-pipeline-debugging.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 | $0.00042 | $0.00528 |
| Opus 5.5 | $0.00017 | $0.00211 |
| Sonnet 5.5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
training-pipeline-debugging 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 1mo 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
ML training bugs are often silent mathematical errors rather than explicit code crashes. This protocol localizes the standard failure classes quickly.
When to Use
- Loss becomes NaN or diverges
- Training crashes with out-of-memory errors
- Shape/dimension errors at matrix multiplications or loss computation
- The pipeline runs but the model never learns
Inputs
- The failing training script and its error output or loss curve
Workflow
- Run the overfit test first: fit a single batch of ~10 examples. If the model cannot achieve near-zero loss on a single batch, the pipeline is fundamentally broken — do not debug full runs until the single-batch test passes.
- NaN losses — check:
- Learning rate too high?
- Missing data (NaNs in input)?
- Log/Exp/Divide-by-zero in custom loss functions?
- Exploding gradients (clip gradients)?
- OOM (out of memory):
- Reduce batch size.
- Check for memory leaks in the training loop (e.g., accumulating history across epochs without
.detach()).
- Shape mismatches:
- Add temporary print statements or assertions on
tensor.shapebefore matrix multiplications and loss calculations.
- Add temporary print statements or assertions on
Output
- The identified failure class and its specific fix, or a single-batch reproduction showing exactly where the pipeline breaks
Verification
- Single-batch overfit test run before any full-run debugging
- For NaN: learning rate, input NaNs, unsafe math, and gradient explosion all checked
- For OOM: batch size and training-loop accumulation both checked
- For shapes: assertions placed before matmul/loss sites
- Fix validated by re-running the previously failing case
Failure Modes
- Debugging full runs first — hours per iteration; the single-batch test gives answers in minutes.
- Treating NaN as random — NaN losses have a small set of causes; check all four systematically instead of restarting with a lower LR.
- Batch-size-only OOM fixes — shrinking the batch hides a loop leak that will OOM again later; check accumulation too.
- Leftover instrumentation — remove temporary shape prints/assertions once the fix is validated.
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.
- 1mo ago First seen · 53 lines · 42 tokens per session scan A accac340a4a8
training-pipeline-debugging is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 42 tokens to every session and 528 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-14.
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