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Found 10 Skills
Comprehensive observability and monitoring skill covering Prometheus, Grafana, metrics collection, alerting, exporters, PromQL, and production monitoring patterns for distributed systems and cloud-native applications
Complete observability stack with structured logging, error tracking, and web analytics.
You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based practices. Design SLO frameworks, define SLIs, and build monitoring that balances reliability with delivery velocity.
You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Create, update, and manage Slot deployments for Katana and Torii services.
Build and deploy a Coralogix dashboard for a given service from its logs, spans, metrics, and service specs. Discovers telemetry through the sibling `cx-metrics-query` / `cx-query-logs` / `cx-query-spans` skills, emits importable Coralogix JSON, verifies every PromQL and DataPrime query live through the `cx` CLI, and creates the dashboard via `cx dashboards create`. Use whenever the user asks to create, build, generate, or deploy a Coralogix dashboard, monitoring dashboard, or observability dashboard for a service, app, or pipeline.
Orchestrate multi-service AWS workflows with autonomous agents. Coordinates across compute, storage, identity, and observability services for intelligent cloud automation.
Build production-ready gRPC services in Go with mTLS, streaming, and observability. Use when designing Protobuf contracts with Buf or implementing secure service-to-service transport.
Consult this skill when designing data pipelines or transformation workflows. Use when data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial. Do not use when selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow is not sequential or predictable. DO NOT use when: complex branching/merging logic dominates.