Loading...
Loading...
Found 473 Skills
Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.
Analyzes observability data — logs, traces, errors, sessions, and metrics — to find root cause and actionable evidence. Use when the user reports a bug, an unexpected behavior, or asks about patterns across application data.
Creates observability dashboards and graphs from logs, traces, errors, sessions, metrics, and events data by previewing charts inline and saving them to a dashboard.
Enterprise skill for iOS production error observability and logging (iOS 15+, Swift 5.5+). Use this skill when writing or reviewing error handling code, adding logging to iOS apps, replacing print() with os.Logger, configuring crash reporting SDKs (Sentry, Crashlytics, PostHog), fixing silent error patterns (try?, Task {} swallowing errors, Combine pipelines dying), adding privacy annotations to logs, integrating MetricKit, implementing retry logic with observability, handling errors in SwiftUI .task {} modifiers, or auditing catch blocks for proper error reporting. Use this skill any time someone writes a catch block, uses try?, creates a Task {}, sets up error handling, or mentions logging, crash reporting, or error tracking in an iOS context — even if they just say 'add error handling' or 'why is this failing silently.'
Grafana Cloud cost management — usage monitoring, cost attribution by label, usage alerts, invoice management, and optimization strategies. Covers Adaptive Metrics (cardinality reduction), Adaptive Logs (log filtering), cost attribution labels, and the FOCUS-compliant billing application. Use when analyzing Grafana Cloud spending, setting up cost alerts, attributing costs to teams, reducing metric/log cardinality, or forecasting observability budgets.
Integrate Databuddy analytics into applications using the SDK or REST API. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, or querying analytics data programmatically.
Use when building cloud-native apps. Keywords: kubernetes, k8s, docker, container, grpc, tonic, microservice, service mesh, observability, tracing, metrics, health check, cloud, deployment, 云原生, 微服务, 容器
Setup Spanora AI observability in any project (JavaScript/TypeScript or Python). Use when user asks to "add spanora", "setup spanora", "integrate spanora", "add AI observability", "monitor LLM calls with spanora", "track AI costs", or mentions spanora in the context of adding observability to their project. Detects the language and installed AI SDKs (Vercel AI, Anthropic, OpenAI, LangChain) and configures the optimal integration pattern.
Query and analyze Coralogix Real User Monitoring (RUM) data. Use this skill when the user asks about frontend errors, page load times, web vitals, user interactions, browser errors, mobile crashes, Core Web Vitals (LCP, CLS, FID, INP, TTFB), JavaScript exceptions, page performance, session errors, RUM data, real user monitoring, or any frontend/client-side observability question - even if they don't explicitly say "RUM".
Complete reference for the Portkey AI Gateway Python SDK with unified API access to 200+ LLMs, automatic fallbacks, caching, and full observability. Use when building Python applications that need LLM integration with production-grade reliability.
Set up and use Dstl8 for observability. Triggers: install or configure Dstl8 (CLI, sources, MCP); incident triage and investigation; root cause analysis; checking whether a deploy fixed an issue; alerting on recurring patterns; cross-environment correlation; pre-coding context on past incidents and recent issues.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.