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Detecting Anomalies with Azure AI Java

azure-ai-anomalydetector-java

sickn33/antigravity-awesome-skills

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

Detecting Anomalies with Azure AI Java

azure-ai-anomalydetector-java

sickn33/antigravity-awesome-skills

This SDK allows developers to build robust anomaly detection applications in Java using Azure AI services. It supports both univariate and multivariate time-series analysis, enabling detection of outliers, trend changes, and correlated signals over large datasets. Ideal for real-time monitoring, operational intelligence, and advanced data quality checks.

LLM Fine-Tuning Guide

fine-tuning-expert

Jeffallan/claude-skills

Guides ML engineers through dataset prep, PEFT strategy selection, LoRA/QLoRA adapter setup, training monitoring, and deployment checks for practical LLM fine-tuning, quantization, and adapter merge workflows.

Multi-Agent MLOps Pipeline Orchestration

machine-learning-ops-ml-pipeline

sickn33/antigravity-awesome-skills

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

Vast.ai Production Deployment Checklist

vastai-prod-checklist

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.

Skin Health Analyzer

skin-health-analyzer

sickn33/antigravity-awesome-skills

Analyze skin health trends, assess acne and mole risks, evaluate skincare and sun protection, and generate personalized prevention and care advice. Useful for routine skin monitoring, skin cancer prevention, and cross-analysis with nutrition, chronic disease, and medication data.

Arxiv Research Fetcher

K-Dense-AI/claude-scientific-skills

Python toolkit that queries the arXiv Atom API to search papers by keywords, authors, categories, or IDs, filter results, download PDFs, and emit structured JSON for AI/ML literature reviews and monitoring.

Analyzing Ransomware Leak Site Intelligence

analyzing-ransomware-leak-site-intelligence

mukul975/Anthropic-Cybersecurity-Skills

This skill provides a structured methodology and Python implementation for monitoring and analyzing data leak sites (DLS) associated with ransomware groups. It helps security analysts track victim postings, extract threat intelligence on group tactics, assess sector-specific risks, and understand evolving ransomware threat landscapes for proactive defense.

Interactive ML Training Health Monitor

wanshuiyin/Auto-claude-code-research-in-sleep

This skill provides continuous, structured monitoring of deep learning model training metrics. It analyzes data from platforms like WandB and local logs to detect critical anomalies such as NaN values, divergence, plateaus, and sudden spikes. By providing a clear decision (CONTINUE, WAIT, or STOP) and detailed evidence, it ensures compute resources are only spent on viable training runs.

Arize LLM Judge and Monitoring

arize-evaluator

github/awesome-copilot

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

Datadog Automation Toolkit

datadog-automation

sickn33/antigravity-awesome-skills

Automate Datadog monitoring tasks via Rube MCP’s Composio toolkit—query metrics, search logs, manage monitors/dashboards, and create events or downtimes while respecting live tool schemas.

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