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Found 69 Skills
Grafana Beyla eBPF auto-instrumentation for application observability without code changes. Covers supported languages/runtimes, requirements, installation, configuration (discovery, eBPF settings, OTLP traces export, Prometheus metrics export), Kubernetes deployment, and integration with Grafana Cloud. Use when setting up zero-code instrumentation, configuring eBPF probes, deploying Beyla to Kubernetes, connecting to Tempo/Prometheus, or troubleshooting instrumentation issues.
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
Instrument LLM applications with Langfuse tracing. Use when setting up Langfuse, adding observability to LLM calls, or auditing existing instrumentation.
Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.
Create a Product-Led Sales Motion Pack (PQL/PQA definition, usage-signal spec + routing/SLA, sales outreach playbook, instrumentation plan, and pilot/scale plan). Use for product-led sales, sales-assist, PLG-to-sales handoffs, and converting self-serve usage into sales opportunities. Category: Sales & GTM.
Apply behavioral science to product design and produce a Behavioral Product Design Pack (target behavior, behavioral diagnosis, intervention map, prioritized concepts, design specs, experiment + instrumentation plan, ethics/trust review). Use for retention, onboarding, habit loops, and behavior change problems.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Clang/LLVM compiler skill for C/C++ projects. Use when working with clang or clang++ for diagnostics, sanitizer instrumentation, optimization remarks, static analysis with clang-tidy, LTO via lld, or when migrating from GCC to Clang. Activates on queries about clang flags, clang-tidy, clang-format, better error messages, Apple/FreeBSD toolchains, or LLVM-specific optimizations. Covers flag selection, diagnostic tuning, and integration with LLVM tooling.
Grafana Cloud Application Observability (APM), Frontend Observability (RUM/Faro), and AI Observability. Covers RED metrics (Rate/Error/Duration), service maps, span metrics from traces, Faro JavaScript/React SDK for browser instrumentation, session replay, AI/LLM model monitoring, and integration with traces/logs/profiles for full-stack correlation. Use when setting up APM, configuring frontend monitoring, analyzing service performance, or monitoring AI/LLM applications.
Remove the DebugBridge SPM package and all #if DEBUG wiring from an iOS app. Cleans up StateServer, DebugOverlay, accessor codegen output, and app-side hooks installed by /ios-qa. This is a convenience wrapper — the structural Release-build guard (Package.swift conditional + CI swift build -c release check) is the safety-critical path. Use when asked to "clean the iOS debug bridge", "remove DebugBridge", or "strip the gstack iOS instrumentation". (gstack) Voice triggers (speech-to-text aliases): "clean the iOS debug bridge", "remove DebugBridge", "strip the gstack iOS instrumentation".
Monitoring and observability strategy, implementation, and troubleshooting. Use for designing metrics/logs/traces systems, setting up Prometheus/Grafana/Loki, creating alerts and dashboards, calculating SLOs and error budgets, analyzing performance issues, and comparing monitoring tools (Datadog, ELK, CloudWatch). Covers the Four Golden Signals, RED/USE methods, OpenTelemetry instrumentation, log aggregation patterns, and distributed tracing.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.