monitoring
Design and operate application observability with metrics, logs, traces, and alerts. Use for SLO definition, dashboard design, on-call runbooks, and incident response.
What this skill does
# Observability Engineering ## Current Versions (Verify Before Use) ```bash prometheus --version # Prometheus server grafana-server -v # Grafana jaeger --version # Jaeger tempo -version # Grafana Tempo ``` ## Core Principles 1. **Metrics for symptoms, logs for causes, traces for paths.** Use the right signal for the right question. 2. **Alert on symptoms, not causes.** Alert when users are affected (error rate ↑, latency ↑), not when a CPU metric crosses a threshold. 3. **SLOs define reliability.** Every service has error budget, SLO targets, and explicit consequences for budget exhaustion. 4. **Dashboards are for exploration, not alerting.** If you need a dashboard to know something is wrong, your alerts are wrong. 5. **Observability data is production code.** Instrumentation gets the same review rigor as business logic. ## SLO Design Template ``` Service: <name> SLI: <ratio of good events / total events> SLO: <target percentage> (e.g., 99.9%) Error Budget: 100% - SLO (e.g., 0.1% = 43.8 min/month) Alerting: - Fast burn: 2% budget in 1 hour → page immediately - Slow burn: 5% budget in 6 hours → page during business hours ``` **SLI types:** - Request-based: `good_requests / total_requests` (availability, latency bucket) - Window-based: `good_time_windows / total_time_windows` (uptime) ## Metric Instrumentation ### RED Method (for services) - **Rate:** Requests per second - **Errors:** Error rate (4xx, 5xx as % of total) - **Duration:** Request latency (p50, p95, p99) ### USE Method (for resources) - **Utilization:** % of resource used (CPU, memory, disk) - **Saturation:** Queue length, wait time - **Errors:** Hardware errors, failed allocations ### The Four Golden Signals 1. Latency 2. Traffic 3. Errors 4. Saturation ## Alert Design Rules - **Page only when human action is required immediately.** Everything else is a ticket or dashboard note. - **Every alert has a runbook.** If there's no runbook, there's no alert. - **Alert fatigue kills observability.** If an alert fires and nobody does anything, delete the alert. - **Use multi-window, multi-burn-rate alerts.** Single-threshold alerts are noisy. ## Common Anti-Patterns | Anti-Pattern | Why It's Wrong | Fix | |---|---|---| | "CPU > 80%" alert | CPU usage is not a user symptom | Alert on latency/error rate, investigate CPU | | Alerting on every error | Not all errors are user-facing | Alert on error rate, not count | | No SLOs | No shared definition of "broken" | Define SLIs and SLOs per service | | Dashboards as primary detection | Reactive, requires human watching | Alert on symptoms, dashboard for diagnosis | | Missing trace context | Can't correlate logs/metrics/traces | Use trace IDs in all signals | | Log everything at INFO | Expensive, noisy, hard to query | Structured logs, sampled debug, ERROR for issues | ## Validation Checklist - [ ] Every service exports RED metrics - [ ] Every service has defined SLOs with error budgets - [ ] Every page alert has a tested runbook - [ ] Alert routing goes to the right team (not a catch-all) - [ ] Dashboards answer "what happened" and "why" for known failure modes - [ ] Traces span service boundaries with propagated context - [ ] Log retention and cost are monitored ## Official Resources - [Google SRE Book — SLOs](https://sre.google/sre-book/service-level-objectives/) - [Prometheus best practices](https://prometheus.io/docs/practices/) - [Grafana alerting docs](https://grafana.com/docs/grafana/latest/alerting/) - [OpenTelemetry](https://opentelemetry.io/docs/) - [Jaeger tracing](https://www.jaegertracing.io/docs/)
Related in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.