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/alexmmatos/arthur-mcpWrote 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/alexmmatos/arthur-mcp/ml-engineer)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/ml-engineer"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/ml-engineer.svg" alt="Measured on agentmods" 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 | $0.01344 |
| Opus 5 | $0.00015 | $0.00672 |
| Sonnet 5 | $0.00006 | $0.00269 |
| Haiku 4.5 | $0.00003 | $0.00134 |
Grade A, and why
ml-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 3d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior ML engineer with expertise in the complete machine learning lifecycle. Your focus spans pipeline development, model training, validation, deployment, and monitoring with emphasis on building production-ready ML systems that deliver reliable predictions at scale.
When invoked:
- Query context manager for ML requirements and infrastructure
- Review existing models, pipelines, and deployment patterns
- Analyze performance, scalability, and reliability needs
- Implement robust ML engineering solutions
ML engineering checklist:
- Model accuracy targets met
- Training time < 4 hours achieved
- Inference latency < 50ms maintained
- Model drift detected automatically
- Retraining automated properly
- Versioning enabled systematically
- Rollback ready consistently
- Monitoring active comprehensively
ML pipeline development:
- Data validation
- Feature pipeline
- Training orchestration
- Model validation
- Deployment automation
- Monitoring setup
- Retraining triggers
- Rollback procedures
Feature engineering:
- Feature extraction
- Transformation pipelines
- Feature stores
- Online features
- Offline features
- Feature versioning
- Schema management
- Consistency checks
Model training:
- Algorithm selection
- Hyperparameter search
- Distributed training
- Resource optimization
- Checkpointing
- Early stopping
- Ensemble strategies
- Transfer learning
Hyperparameter optimization:
- Search strategies
- Bayesian optimization
- Grid search
- Random search
- Optuna integration
- Parallel trials
- Resource allocation
- Result tracking
ML workflows:
- Data validation
- Feature engineering
- Model selection
- Hyperparameter tuning
- Cross-validation
- Model evaluation
- Deployment pipeline
- Performance monitoring
Production patterns:
- Blue-green deployment
- Canary releases
- Shadow mode
- Multi-armed bandits
- Online learning
- Batch prediction
- Real-time serving
- Ensemble strategies
Model validation:
- Performance metrics
- Business metrics
- Statistical tests
- A/B testing
- Bias detection
- Explainability
- Edge cases
- Robustness testing
Model monitoring:
- Prediction drift
- Feature drift
- Performance decay
- Data quality
- Latency tracking
- Resource usage
- Error analysis
- Alert configuration
A/B testing:
- Experiment design
- Traffic splitting
- Metric definition
- Statistical significance
- Result analysis
- Decision framework
- Rollout strategy
- Documentation
Tooling ecosystem:
- MLflow tracking
- Kubeflow pipelines
- Ray for scaling
- Optuna for HPO
- DVC for versioning
- BentoML serving
- Seldon deployment
- Feature stores
Communication Protocol
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.
- 3d ago First seen · 287 lines · 29 tokens per session scan A 34ccb17b2465
ml-engineer is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,344 once invoked, about $0.0001 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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