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Found 337 Skills
Production server monitoring stack covering Prometheus, Node Exporter, Grafana, Alertmanager, Loki, and Promtail on bare-metal or VM Linux hosts. USE WHEN: - Setting up monitoring for a new production server or VPS - Configuring Prometheus scrape targets for application or system metrics - Creating Grafana dashboards and datasource provisioning - Writing Alertmanager routing rules with email/Slack notifications - Implementing the PLG stack (Promtail + Loki + Grafana) for log aggregation - Performing live system diagnostics with htop, iotop, nethogs, ss, vmstat, iostat - Setting up uptime monitoring with UptimeRobot or healthchecks.io DO NOT USE FOR: - Kubernetes-native observability (use the kubernetes skill instead) - Application-level APM (distributed tracing with Jaeger/Tempo — use observability skill) - Cloud-managed monitoring (CloudWatch, GCP Monitoring, Azure Monitor) - Windows Server monitoring
Cloudflare Workers observability with logging, Analytics Engine, Tail Workers, metrics, and alerting. Use for monitoring, debugging, tracing, or encountering log parsing, metric aggregation, alert configuration errors.
Debug and investigate code issues using search and AI analysis. Use when stuck on bugs, tracing execution flow, or understanding complex code.
Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).
Generate Chi HTTP handlers following GO modular architechture conventions (request/response DTOs, use case orchestration, error handling, swagger annotations, Fx DI). Use when creating HTTP endpoint handlers in internal/modules/<module>/http/chi/handler/ for REST operations (List, Create, Update, Delete, Get) that need to decode requests, call use cases, map responses, and handle errors with proper logging and tracing.
Instrument applications with OpenTelemetry SDK and validate telemetry using Kopai. Use when setting up observability, adding tracing/logging/metrics, testing instrumentation, or debugging missing telemetry data.
World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.
Vercel observability for Web Analytics, Speed Insights, logs, tracing, alerts, and observability tooling. Use when monitoring performance or debugging production behavior on Vercel.
General guidance for building Rust CLI programs (clap/anyhow/tracing/serde_json), with agent-friendly patterns: JSON output mode, stdout/stderr separation, predictable exit codes, integration testing, and a cargo-release based release workflow.
Instrument a Java application with the Elastic Distribution of OpenTelemetry (EDOT) Java agent for automatic tracing, metrics, and logs. Use when adding observability to a Java service that has no existing APM agent.
OpenAI Codex Rust coding patterns distilled from the codex-rs workspace. Use this skill whenever writing, reviewing, or refactoring Rust code — especially for async agents, CLI tools, sandboxing, Ratatui TUIs, JSON-RPC protocols, tokio-based services, or any codebase that needs defensive panic discipline. Trigger even when the user does not explicitly mention Codex, because the patterns generalize to any production Rust workspace. Covers async cancellation, error enum design, process sandboxing, Cargo workspace architecture, wiremock-based fakes, insta snapshot testing, OpenTelemetry tracing, and Ratatui rendering.
Integration guide for Morph's WarpGrep (fast agentic code search) and Fast Apply (10,500 tok/s code editing). Use when building coding agents that need fast, accurate code search or need to apply AI-generated edits to code efficiently. Particularly useful for large codebases, deep logic queries, bug tracing, and code path analysis.