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 ancoleman/ai-design-components --skill implementing-mlopsgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/implementing-mlops)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/implementing-mlops"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/implementing-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/ancoleman/ai-design-components/implementing-mlops"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/implementing-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 | $0.00108 | $0.08432 |
| Opus 5.5 | $0.00043 | $0.03373 |
| Sonnet 5.5 | $0.00022 | $0.01686 |
| Haiku 4.5 | $0.00011 | $0.00843 |
Grade A, and why
implementing-mlops 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.
How it starts
The opening of the file, as written. The whole thing — 961 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Patterns
Operationalize machine learning models from experimentation to production deployment and monitoring.
Purpose
Provide strategic guidance for ML engineers and platform teams to build production-grade ML infrastructure. Cover the complete lifecycle: experiment tracking, model registry, feature stores, deployment patterns, pipeline orchestration, and monitoring.
When to Use This Skill
Use this skill when:
- Designing MLOps infrastructure for production ML systems
- Selecting experiment tracking platforms (MLflow, Weights & Biases, Neptune)
- Implementing feature stores for online/offline feature serving
- Choosing model serving solutions (Seldon Core, KServe, BentoML, TorchServe)
- Building ML pipelines for training, evaluation, and deployment
- Setting up model monitoring and drift detection
- Establishing model governance and compliance frameworks
- Optimizing ML inference costs and performance
- Migrating from notebooks to production ML systems
- Implementing continuous training and automated retraining
Core Concepts
1. Experiment Tracking
Track experiments systematically to ensure reproducibility and collaboration.
Key Components:
- Parameters: Hyperparameters logged for each training run
- Metrics: Performance measures tracked over time (accuracy, loss, F1)
- Artifacts: Model weights, plots, datasets, configuration files
- Metadata: Tags, descriptions, Git commit SHA, environment details
Platform Comparison:
MLflow (Open-source standard):
- Framework-agnostic (PyTorch, TensorFlow, scikit-learn, XGBoost)
- Self-hosted or cloud-agnostic deployment
- Integrated model registry
- Basic UI, adequate for most use cases
- Free, requires infrastructure management
Weights & Biases (SaaS, collaboration-focused):
- Advanced visualization and dashboards
- Integrated hyperparameter optimization (Sweeps)
- Excellent team collaboration features
- SaaS pricing scales with usage
- Best-in-class UI
Neptune.ai (Enterprise-grade):
- Enterprise features (RBAC, audit logs, compliance)
- Integrated production monitoring
- Higher cost than W&B
- Good for regulated industries
What ships with it
18 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.
- examples/bentoml_model_serving.py 7.5 KB runs code
- examples/feast_feature_store.py 9.0 KB runs code
- examples/kubeflow_pipeline.py 10 KB runs code
- examples/mlflow_experiment_tracking.py 5.4 KB runs code
- examples/model_monitoring.py 18 KB runs code
- outputs.yaml 9.7 KB
- references/decision-frameworks.md 19 KB
- references/deployment-strategies.md 30 KB
- references/experiment-tracking.md 16 KB
- references/feature-stores.md 24 KB
- references/governance.md 30 KB
- references/llmops-patterns.md 27 KB
- references/ml-pipelines.md 20 KB
- references/model-monitoring.md 20 KB
- references/model-registry.md 18 KB
- references/model-serving.md 17 KB
- references/scenarios.md 18 KB
- references/tool-recommendations.md 21 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.
- 1mo ago First seen · 961 lines · 108 tokens per session scan A cff7a970aa2b
implementing-mlops is a skill published in the GitHub repository ancoleman/ai-design-components (524 stars, last pushed 10mo ago), licensed MIT. It adds 108 tokens to every session and 8,432 once invoked, about $0.0004 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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