Skill UI

This guide provides a comprehensive method for setting up Snowflake observability. It teaches how to leverage built-in ACCOUNT_USAGE views to monitor critical metrics such as query performance (latency, error rates), credit consumption, and storage growth. Furthermore, it demonstrates how to implement automated alerts for operational issues (e.g., high failure rates, stuck warehouses) and export metrics to external monitoring systems like Grafana or Prometheus.

TweetClaw provides comprehensive automation capabilities for X/Twitter using the Xquik REST API. It allows users to perform full read and write operations—including posting, replying, liking, following, and sending DMs. Key features include advanced tweet searching, bulk data scraping (23 tools), account monitoring, running structured giveaways, and composing algorithm-optimized content. Ideal for social media managers, digital marketers, and data analysts needing deep platform insights.

Implement a comprehensive observability stack for Webflow Data API v2 integrations. This skill provides structured code using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging (pino) to monitor API call frequency, latency, error rates, and rate limit usage. Ideal for developers building robust, production-grade Webflow-connected SaaS applications that require deep performance insights and reliable alerting.

This skill provides high-performance, real-time object detection utilizing the Google Coral Edge TPU accelerator. It processes live camera frames to identify up to 80 COCO classes (e.g., person, car, dog). Optimized for native macOS and Linux deployment, it achieves extremely low latency (e.g., ~4ms at 320x320), making it ideal for embedded vision systems, surveillance, and low-latency monitoring applications.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

This engine aggregates news from multiple RSS sources (e.g., TechCrunch, The Verge) and performs advanced sentiment analysis using AI (Claude). It ranks articles by industry impact, generates structured summaries, and provides actionable commentary, making it ideal for daily tech/AI briefing preparation and market monitoring.

This comprehensive guide details the programmatic management of semantic model refreshes using the Power BI Enhanced Refresh REST API and Fabric CLI. It covers the entire refresh lifecycle, including resolving model IDs, validating baseline data, triggering various refresh types (full, data-only, calculate), monitoring status, and performing robust post-refresh validation. Essential for automating ETL and data pipeline processes in a modern data platform.

Falco is a powerful CNCF project for real-time runtime security monitoring. This guide teaches authors how to write and deploy custom detection rules using Falco's robust YAML rule engine and modern eBPF driver. It is designed to detect critical threats such as container escapes, namespace manipulation, privileged mounts, and anomalous syscalls in Kubernetes and Docker environments, significantly hardening the overall container security posture.

A comprehensive skill for systematically monitoring developer conversations across technical platforms like GitHub, Reddit, and Stack Overflow. It helps track brand mentions, competitor positioning, and unsolved technical problems to gather unfiltered product feedback and high-intent leads.

Utilize this skill to assess the health, adoption, and performance metrics of published EAS Updates directly via the CLI. It reports critical data points including crash rates, unique user counts, install volumes, payload size, and the split between embedded and OTA users, making it essential for post-publish monitoring and regression detection.

EAS Observe is a comprehensive tool for monitoring performance metrics in production Expo applications. It tracks critical performance data, including app startup time, navigation performance across routes, and custom user events. Developers can integrate it into their Expo project using the `expo-observe` package and query detailed reports via the EAS CLI, ensuring optimal user experience and stability.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.