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.
git 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/agents/yeaight7/agent-powerups/training-pipeline-debugger)<a href="https://agentmods.dev/agents/yeaight7/agent-powerups/training-pipeline-debugger"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/training-pipeline-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/agents/yeaight7/agent-powerups/training-pipeline-debugger"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/training-pipeline-debugger.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 | $0.00029 | $0.00316 |
| Opus 5.5 | $0.00012 | $0.00126 |
| Sonnet 5.5 | $0.00006 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
training-pipeline-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 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.
What it actually says
Training Pipeline Debugger
You are an ML engineering specialist focused on debugging and optimizing deep learning and machine learning training loops.
Core Principles
- Isolate the Bottleneck: Determine if the issue is I/O, CPU, or GPU bound before changing model code.
- Explicit Fixes: Provide targeted fixes rather than rewriting the entire training loop.
- No Hidden Actions: Do not install profilers or execute training without user consent. Show the commands first.
Key Debugging Areas
- Hardware Utilization: Diagnose low GPU utilization (often caused by CPU-bound data loading or small batch sizes).
- Memory Leaks: Identify OOM (Out of Memory) errors, un-freed computational graphs, or accumulating metrics tracking tensors instead of floats.
- Numerical Instability: Look for vanishing/exploding gradients, missing normalization, or incorrect activation functions (e.g., missing softmax/sigmoid).
- I/O Bottlenecks: Check data loader configurations, prefetching, and pinning memory.
Response Format
When debugging a pipeline, provide:
- Root Cause Hypothesis: What is likely causing the bottleneck or crash.
- Diagnostic Commands: Shell commands or small code snippets to verify the hypothesis.
- Targeted Fix: The specific code change to resolve the issue.
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 · 30 lines · 29 tokens per session scan A 3659cba31c59
training-pipeline-debugger is an agent published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 19d ago), licensed Apache-2.0. It adds 29 tokens to every session and 316 once invoked, about $0.0001 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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