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Found 487 Skills
Scheduler and background jobs syntax for Frappe/ERPNext v14/v15/v16. Use for scheduler_events in hooks.py, frappe.enqueue() for async jobs, queue configuration, job deduplication, error handling, and monitoring. Triggers on questions about scheduled tasks, background processing, cron jobs, RQ workers, job queues, async tasks.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Build custom AI search monitoring tools for competitive AEO analysis. Covers API access, scraping architecture, legal compliance, and cost estimation.
Comprehensive observability and monitoring skill covering Prometheus, Grafana, metrics collection, alerting, exporters, PromQL, and production monitoring patterns for distributed systems and cloud-native applications
Sentry error monitoring and issue tracking skill for retrieving issues, events, and project health data. Use when working with error tracking, exceptions, crashes, debugging production issues, or analyzing error patterns.
Search and analyze DealerVision production logs via SolarWinds Observability API. Use when investigating errors, debugging issues, checking system health, or when the user mentions logs, SolarWinds, production errors, or system monitoring. Requires the `logs` CLI tool to be installed.
LinkedIn Content-Erstellung, Engagement und Monitoring für B2B/Manufacturing. Regionale Anpassung (US/EU/Asien), Artikel mit Teasern, Bildgenerierung via Gemini, Kommentar-Monitoring.
Expert-level Prometheus monitoring, metrics collection, PromQL queries, alerting, and production operations
Optimizes web application performance through code splitting, lazy loading, caching strategies, and Core Web Vitals monitoring. Use when improving page load times, implementing service workers, or reducing bundle sizes.
This skill should be used when users need to interact with Kubernetes clusters via kubectl CLI. It covers pod management, deployment operations, log viewing, debugging, resource monitoring, scaling, ConfigMaps, Secrets, Services, and all standard kubectl operations. Supports multiple clusters (production, staging, local k3s) with predefined aliases. Triggers on requests mentioning Kubernetes, k8s, pods, deployments, containers, or cluster operations.
Observability and SRE expert. Use when setting up monitoring, logging, tracing, defining SLOs, or managing incidents. Covers Prometheus, Grafana, OpenTelemetry, and incident response best practices.
Proactive context window management via token monitoring, intelligent extraction, and selective rehydration. Features predictive budget monitoring, context health indicators, and priority-based retention. Use when approaching token limits or needing to preserve essential context. Complements /transcripts and PreCompact hook with proactive optimization.