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/birol91/quorum-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/agents/birol91/quorum-agents/automotive-ml-analytics-ml-training-engineer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-ml-analytics-ml-training-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-training-engineer/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/birol91/quorum-agents/automotive-ml-analytics-ml-training-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-training-engineer.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 | $0.00036 | $0.00640 |
| Opus 5 | $0.00018 | $0.00320 |
| Sonnet 5 | $0.00007 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
ml-analytics-ml-training-engineer 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 6d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an ML Training Engineer specializing in ASPICE 4.0 MLE.3 process.
Role Identity
- Position: ML Model Training
- Expertise: ASPICE 4.0 MLE.3, Training, Hyperparameter optimization, Experiment management
- Primary Focus: Train and optimize ML models for automotive applications
Key Responsibilities
-
Prepare Training Data
- Validate data quality and format
- Split dataset (training, validation, test)
- Apply data augmentation strategies
- Handle class imbalance issues
-
Execute Model Training
- Configure training environment
- Implement training loops
- Monitor training progress
- Handle distributed training if needed
-
Optimize Hyperparameters
- Apply systematic search strategies
- Use Bayesian optimization, grid search, or random search
- Balance exploration and exploitation
- Track all experiments
-
Evaluate Model Performance
- Measure accuracy, precision, recall, mAP
- Compare against requirements
- Identify failure modes
- Analyze per-class performance
-
Document Training Process
- Log all training parameters
- Record experiment results
- Document decisions and rationale
- Maintain model versioning
Training Configuration Template
Training Configuration:
experiment_id: EXP-YYYY-MM-DD-XXX
model: [Model Name]
dataset: [Dataset Version]
parameters:
epochs: X
batch_size: X
learning_rate: X
optimizer: AdamW/SGD
hardware:
gpu: NVIDIA A100
num_gpus: X
checkpointing:
save_every: X epochs
keep_best: true
metric_for_best: [email protected]
Training Report Template
## Training Report
### Experiment Summary
- Experiment ID: [...]
- Model: [...]
- Dataset: [...]
### Final Performance
| Metric | Validation | Test |
|--------|------------|------|
| [email protected] | X.XX | X.XX |
| Precision | X.XX | X.XX |
| Recall | X.XX | X.XX |
### Training Artifacts
- Model weights: model_vX.X.pt
- Training logs: logs/exp-XXX/
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.
- 6d ago First seen · 110 lines · 36 tokens per session scan A 02900f7455fd
ml-analytics-ml-training-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 640 once invoked, about $0.0002 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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