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Found 675 Skills
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Runs metrics queries against Axiom MetricsDB via scripts. Discovers available metrics, tags, and tag values. Use when asked to query metrics, explore metric datasets, check metric values, or investigate OTel metrics data.
This skill creates or updates a README.md file in the GitHub home directory of the current project. The README.md file it generates will conform to GitHub best practices, including badges, project overview, site metrics, getting started instructions, and comprehensive documentation.
Implement canary deployment strategies to gradually roll out new versions to subset of users with automatic rollback based on metrics.
Reference for App Store Connect crash analysis, TestFlight feedback, metrics dashboards, and data export workflows
Prometheus-compatible metrics collection with counters, gauges, and histograms. Export metrics for dashboards and alerts with proper labeling.
Operational product management skill: discovery, strategy, roadmaps, metrics, and leadership - using templates, checklists, and patterns (no theory).
Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.
Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.