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 yezannnnn/agentGroup --skill senior-ml-engineergit clone --depth 1 https://github.com/yezannnnn/agentGroupWrote 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/yezannnnn/agentgroup/senior-ml-engineer)<a href="https://agentmods.dev/skills/yezannnnn/agentgroup/senior-ml-engineer"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/senior-ml-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/skills/yezannnnn/agentgroup/senior-ml-engineer"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/senior-ml-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 | $0.00045 | $0.02127 |
| Opus 5 | $0.00023 | $0.01064 |
| Sonnet 5 | $0.00009 | $0.00425 |
| Haiku 4.5 | $0.00005 | $0.00213 |
Grade A, and why
senior-ml-engineer scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1 How it starts
The opening of the file, as written. The whole thing — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior ML Engineer
Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.
Table of Contents
- Model Deployment Workflow
- MLOps Pipeline Setup
- LLM Integration Workflow
- RAG System Implementation
- Model Monitoring
- Reference Documentation
- Tools
Model Deployment Workflow
Deploy a trained model to production with monitoring:
- Export model to standardized format (ONNX, TorchScript, SavedModel)
- Package model with dependencies in Docker container
- Deploy to staging environment
- Run integration tests against staging
- Deploy canary (5% traffic) to production
- Monitor latency and error rates for 1 hour
- Promote to full production if metrics pass
- Validation: p95 latency < 100ms, error rate < 0.1%
Container Template
FROM python:3.11-slim
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY model/ /app/model/
COPY src/ /app/src/
HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1
EXPOSE 8080
CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "8080"]
Serving Options
| Option | Latency | Throughput | Use Case |
|---|---|---|---|
| FastAPI + Uvicorn | Low | Medium | REST APIs, small models |
| Triton Inference Server | Very Low | Very High | GPU inference, batching |
| TensorFlow Serving | Low | High | TensorFlow models |
| TorchServe | Low | High | PyTorch models |
| Ray Serve | Medium | High | Complex pipelines, multi-model |
MLOps Pipeline Setup
Establish automated training and deployment:
- Configure feature store (Feast, Tecton) for training data
- Set up experiment tracking (MLflow, Weights & Biases)
- Create training pipeline with hyperparameter logging
- Register model in model registry with version metadata
- Configure staging deployment triggered by registry events
- Set up A/B testing infrastructure for model comparison
- Enable drift monitoring with alerting
- Validation: New models automatically evaluated against baseline
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 305 lines · 45 tokens per session scan A 9d37b6e6396c
senior-ml-engineer is a skill published in the GitHub repository yezannnnn/agentGroup (149 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 2,127 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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