Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: anomalies

英語版ディレクトリ

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
信頼
カテゴリ: research監査

Local security audit for AI API relays and LLM proxies: detects prompt injection, model substitution, tool-call rewriting, SSE anomalies, error leakage, and Web3 wallet risks.

709
Stars
68/100
信頼
カテゴリ: agent-frameworks監査

Reduce logs to their semantic anomalies

157
Stars
68/100
信頼
カテゴリ: devops監査

Alaz: Advanced eBPF Agent for Kubernetes Observability – Effortlessly monitor K8s service interactions and performance metrics in your K8s environment. Gain in-depth insights with service maps, metrics, and more, while staying alert to crucial system anomalies 🐝

716
Stars
66/100
信頼
カテゴリ: devops監査

Diagnose why a SigNoz alert fired by correlating the alert's own signal with neighbor signals (error rate, latency, throughput, CPU/memory), traces, and logs around the fire window — and rank likely causes. Make sure to use this skill whenever the user asks "why did this alert fire", "what caused alert X", "investigate this alert", "RCA for the alert that paged me", "what's wrong with [service]" in the context of a recent fire, or otherwise asks for a root-cause analysis of a firing or recently-fired alert. Read-only — does not modify any alert or notification.

15
Stars
58/100
信頼
カテゴリ: automation監査