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 williamzujkowski/standards --skill mlopsgit clone --depth 1 https://github.com/williamzujkowski/standardsWrote 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/williamzujkowski/standards/mlops)<a href="https://agentmods.dev/skills/williamzujkowski/standards/mlops"><img src="https://agentmods.dev/badge/skills/williamzujkowski/standards/mlops/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/williamzujkowski/standards/mlops"><img src="https://agentmods.dev/badge/skills/williamzujkowski/standards/mlops.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 | $0.03437 |
| Opus 5.5 | $0.00018 | $0.01375 |
| Sonnet 5.5 | $0.00009 | $0.00687 |
| Haiku 4.5 | $0.00005 | $0.00344 |
Grade A, and why
mlops 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 2d 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.
curl -X POST http://localhost:8080/predictions/my_model -T input.json How it starts
The opening of the file, as written. The whole thing — 541 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps (Machine Learning Operations)
Overview
MLOps brings DevOps principles to machine learning workflows, enabling reliable, scalable, and reproducible ML systems in production. It covers the entire ML lifecycle from experimentation to deployment and monitoring.
Core Principles:
- Reproducibility: Version everything (code, data, models, environments)
- Automation: Automate training, testing, deployment pipelines
-
- Monitoring: Track model performance, data drift, system health
- Collaboration: Bridge data scientists, engineers, and operations
- Governance: Ensure model compliance, explainability, and auditing
Level 1: Quick Reference
ML Lifecycle Stages
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Data │────▶│ Training │────▶│ Deployment │────▶│ Monitoring │
│ Collection │ │ & Experiment│ │ & Serving │ │ & Retraining│
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │ │ │
│ │ │ │
▼ ▼ ▼ ▼
Versioning Tracking Inference Drift Detection
Validation Reproducibility Scalability Performance Decay
Feature Eng. Hyperparameters A/B Testing Alerts & Triggers
MLOps vs Traditional DevOps
| Aspect | Traditional DevOps | MLOps |
|---|---|---|
| Artifacts | Code, binaries | Code + Data + Models + Features |
| Testing | Unit, integration tests | Data validation + Model evaluation + Inference tests |
| Deployment | Deploy once, stable | Continuous retraining, model decay |
| Monitoring | Logs, metrics, traces | + Data drift, concept drift, model performance |
| Versioning | Git for code | Git + DVC for data + Model registry |
| Reproducibility | Dockerfile, env vars | + Data versions, random seeds, feature pipelines |
What ships with it
12 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.
- README.md 3.8 KB
- REFERENCE.md 25 KB
- scripts/drift-detection.py 13 KB runs code
- templates/ab-testing-framework.py 13 KB runs code
- templates/feature-store.py 10 KB runs code
- templates/kubeflow-pipeline.yaml 8.4 KB
- templates/mlflow-project/conda.yaml 338 B
- templates/mlflow-project/MLproject 941 B
- templates/mlflow-project/README.md 1.2 KB
- templates/mlflow-project/train.py 6.7 KB runs code
- templates/mlflow-project/validate.py 2.5 KB runs code
- templates/model-serving-config.yaml 6.3 KB
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.
- 2d ago First seen · 541 lines · 45 tokens per session scan A 31039d432220
mlops is a skill published in the GitHub repository williamzujkowski/standards (18 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 3,437 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-29.
Other skills, from other repositories
mlops
MLOps infrastructure engineering covering model serving (vLLM, TorchServe, Triton), feature stores, model registries (MLflow, W&B), A/B and shadow deployments, drift detection, Prometheus/Grafana ML metrics, Kubeflow Pipelines, and Airflow DAGs for end-to-end training and deployment pipelines.
mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform.
setup
Configure MLflow tracing for Claude Code.
ml-expert
Expert-level machine learning, deep learning, model training, and MLOps. Use when the user mentions machine learning, deep learning, neural networks, MLOps, or data science, or when the task involves Machine Learning Fundamentals, Data Preparation, or Model Training.
mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform.
mle-workflow
Production ML engineering workflow — data contracts, reproducible training, evaluation gates, deployment, and monitoring. Use when building, reviewing, or hardening ML systems beyond notebooks.