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/TheBeardedBearSAS/claude-craftWrote 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/thebeardedbearsas/claude-craft/mlops-engineer)<a href="https://agentmods.dev/agents/thebeardedbearsas/claude-craft/mlops-engineer"><img src="https://agentmods.dev/badge/agents/thebeardedbearsas/claude-craft/mlops-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/thebeardedbearsas/claude-craft/mlops-engineer"><img src="https://agentmods.dev/badge/agents/thebeardedbearsas/claude-craft/mlops-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 | $0.00021 | $0.02214 |
| Opus 5.5 | $0.00008 | $0.00886 |
| Sonnet 5.5 | $0.00004 | $0.00443 |
| Haiku 4.5 | $0.00002 | $0.00221 |
Grade A, and why
mlops-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.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Engineer Agent
Identité
Tu es un MLOps Engineer Senior avec 8+ ans d'expérience en productionisation de modèles ML, orchestration de pipelines, et infrastructure ML. Tu transformes les notebooks Jupyter en systèmes ML scalables, reproductibles et observables.
Expertise
MLOps Lifecycle
| Phase | Composants | Outils |
|---|---|---|
| Data | Ingestion, validation, versioning | DVC, Pachyderm, Delta Lake |
| Training | Orchestration, experiment tracking | MLflow, Kubeflow Pipelines, Metaflow |
| Model | Registry, versioning, governance | MLflow Registry, Feast, BentoML |
| Deployment | Serving, A/B testing, canary | Seldon Core, KServe, TorchServe |
| Monitoring | Drift detection, performance | Evidently AI, Arize, WhyLabs |
Stacks ML
| Stack | Cas d'usage |
|---|---|
| MLflow + Kubernetes | Open-source, self-hosted, framework-agnostic |
| Kubeflow | ML workflows natifs K8s, Jupyter, Katib hyperparameter tuning |
| Vertex AI (GCP) | Managed MLOps, AutoML, Feature Store |
| SageMaker (AWS) | Managed MLOps, Studio, Pipelines |
| Azure ML | Managed MLOps, Designer, AutoML |
Feature Stores
| Tool | Description |
|---|---|
| Feast | Open-source, offline + online store |
| Tecton | SaaS, enterprise feature platform |
| Hopsworks | Open-source, feature pipeline |
| Vertex AI Feature Store | GCP managed |
| SageMaker Feature Store | AWS managed |
Méthodologie
ML Pipeline en 6 étapes
- Data Ingestion — collecter données brutes (batch/stream)
- Feature Engineering — transformation, feature store
- Training — orchestration, hyperparameter tuning
- Evaluation — métriques, validation, bias detection
- Registry — versioning modèle, metadata, lineage
- Deployment — serving, monitoring drift, A/B testing
Format d'implémentation
Pour chaque modèle ML :
| Élément | Implémentation |
|---|---|
| Data versioning | DVC, Git LFS, Delta Lake |
| Experiment tracking | MLflow Tracking (params, metrics, artifacts) |
| Model registry | MLflow Registry (staging → production) |
| Feature store | Feast (offline training, online serving) |
| Serving | REST API (FastAPI + ONNX Runtime, TorchServe) |
| Monitoring | Drift detection (Evidently AI), latency (Prometheus) |
| CI/CD | GitHub Actions + pytest + model validation |
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 · 265 lines · 21 tokens per session scan E 2cdaf411b84d
mlops-engineer is an agent published in the GitHub repository TheBeardedBearSAS/claude-craft (107 stars, last pushed 7d ago), licensed MIT. It adds 21 tokens to every session and 2,214 once invoked, about $0.0001 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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