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 MonumentalSystems/Atlas-Agent-Teams --skill mlops-pipelinesgit clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWrote 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/monumentalsystems/atlas-agent-teams/mlops-pipelines)<a href="https://agentmods.dev/skills/monumentalsystems/atlas-agent-teams/mlops-pipelines"><img src="https://agentmods.dev/badge/skills/monumentalsystems/atlas-agent-teams/mlops-pipelines/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/monumentalsystems/atlas-agent-teams/mlops-pipelines"><img src="https://agentmods.dev/badge/skills/monumentalsystems/atlas-agent-teams/mlops-pipelines.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 | $0.00028 | $0.01497 |
| Opus 5 | $0.00014 | $0.00749 |
| Sonnet 5 | $0.00006 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
mlops-pipelines 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 12d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Pipelines
Model Deployment Strategies
Batch Deployment
- Description: Run model on fixed schedule on accumulated data
- Use Cases: Credit scoring, churn prediction, recommendations
- Advantages: Simple, cost-effective, handles large volumes
- Challenges: Latency, stale predictions
- Tools: Apache Airflow, dbt, cron jobs, cloud batch services
Real-time Deployment
- Description: Serve model as API for immediate predictions
- Use Cases: Fraud detection, dynamic pricing, personalization
- Advantages: Low latency, fresh predictions
- Challenges: Scalability, infrastructure complexity
- Tools: Flask, FastAPI, TensorFlow Serving, TorchServe, KServe
Edge Deployment
- Description: Deploy model on edge devices (IoT, mobile, embedded)
- Use Cases: Computer vision, speech recognition, offline scenarios
- Advantages: Low latency, privacy, no internet required
- Challenges: Limited compute, model size constraints
- Tools: TensorFlow Lite, ONNX, Core ML, ML Kit
Streaming Deployment
- Description: Process data streams with real-time predictions
- Use Cases: Real-time analytics, monitoring, anomaly detection
- Advantages: Continuous processing, low latency
- Challenges: State management, exactly-once semantics
- Tools: Apache Kafka, Apache Flink, Apache Spark Streaming
Model Monitoring and Drift Detection
Performance Monitoring
- Prediction Metrics: Track model outputs and distributions
- Accuracy Metrics: Monitor precision, recall, F1, MAE, RMSE
- Business Metrics: Connect predictions to business KPIs
- Latency: Track prediction response times
- Throughput: Monitor predictions per second
Data Drift Detection
- Covariate Drift: Changes in input feature distribution
- Prior Probability Drift: Changes in target class distribution
- Concept Drift: Changes in relationship between features and target
- Detection Methods: Statistical tests, KL divergence, PSI
- Visualization: Feature distribution plots over time
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.
- 12d ago First seen · 161 lines · 28 tokens per session scan A b322db2714bf
mlops-pipelines is a skill published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,497 once invoked, about $0.0001 per session on Opus 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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