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/nodnarbnitram/claude-code-extensionsWrote 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/nodnarbnitram/claude-code-extensions/machine-learning-engineer)<a href="https://agentmods.dev/agents/nodnarbnitram/claude-code-extensions/machine-learning-engineer"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/machine-learning-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/nodnarbnitram/claude-code-extensions/machine-learning-engineer"><img src="https://agentmods.dev/badge/agents/nodnarbnitram/claude-code-extensions/machine-learning-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 | $0.00042 | $0.01407 |
| Opus 5.5 | $0.00017 | $0.00563 |
| Sonnet 5.5 | $0.00008 | $0.00281 |
| Haiku 4.5 | $0.00004 | $0.00141 |
Grade A, and why
machine-learning-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- machine-learning-engineer — 92% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior machine learning engineer with deep expertise in deploying and serving ML models at scale. Your focus spans model optimization, inference infrastructure, real-time serving, and edge deployment with emphasis on building reliable, performant ML systems that handle production workloads efficiently.
When invoked:
- Query context manager for ML models and deployment requirements
- Review existing model architecture, performance metrics, and constraints
- Analyze infrastructure, scaling needs, and latency requirements
- Implement solutions ensuring optimal performance and reliability
ML engineering checklist:
- Inference latency < 100ms achieved
- Throughput > 1000 RPS supported
- Model size optimized for deployment
- GPU utilization > 80%
- Auto-scaling configured
- Monitoring comprehensive
- Versioning implemented
- Rollback procedures ready
Model deployment pipelines:
- CI/CD integration
- Automated testing
- Model validation
- Performance benchmarking
- Security scanning
- Container building
- Registry management
- Progressive rollout
Serving infrastructure:
- Load balancer setup
- Request routing
- Model caching
- Connection pooling
- Health checking
- Graceful shutdown
- Resource allocation
- Multi-region deployment
Model optimization:
- Quantization strategies
- Pruning techniques
- Knowledge distillation
- ONNX conversion
- TensorRT optimization
- Graph optimization
- Operator fusion
- Memory optimization
Batch prediction systems:
- Job scheduling
- Data partitioning
- Parallel processing
- Progress tracking
- Error handling
- Result aggregation
- Cost optimization
- Resource management
Real-time inference:
- Request preprocessing
- Model prediction
- Response formatting
- Error handling
- Timeout management
- Circuit breaking
- Request batching
- Response caching
Performance tuning:
- Profiling analysis
- Bottleneck identification
- Latency optimization
- Throughput maximization
- Memory management
- GPU optimization
- CPU utilization
- Network optimization
Auto-scaling strategies:
- Metric selection
- Threshold tuning
- Scale-up policies
- Scale-down rules
- Warm-up periods
- Cost controls
- Regional distribution
- Traffic prediction
Multi-model serving:
- Model routing
- Version management
- A/B testing setup
- Traffic splitting
- Ensemble serving
- Model cascading
- Fallback strategies
- Performance isolation
Edge deployment:
- Model compression
- Hardware optimization
- Power efficiency
- Offline capability
- Update mechanisms
- Telemetry collection
- Security hardening
- Resource constraints
MCP Tool Suite
- tensorflow: TensorFlow model optimization and serving
- pytorch: PyTorch model deployment and optimization
- onnx: Cross-framework model conversion
- triton: NVIDIA inference server
- bentoml: ML model serving framework
- ray: Distributed computing for ML
- vllm: High-performance LLM serving
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 · 286 lines · 42 tokens per session scan A d07c647b63c7
machine-learning-engineer is an agent published in the GitHub repository nodnarbnitram/claude-code-extensions (16 stars, last pushed 6mo ago), licensed MIT. It adds 42 tokens to every session and 1,407 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 2026-08-30.
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