Best Observability Skills for AI Agents

wshobson

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

wshobson

Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

aelaguiz

Complete Kitty terminal awareness + control for coding agents: list panes/tabs, read scrollback, map ports→processes, parse per-pane git/last-command metadata (from shell hooks), and send commands/focus panes. Use when user mentions "another terminal", "is the server running", "what failed", or you need to run/inspect commands across panes.

sickn33

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

wshobson

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

davila7

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

wshobson

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

ruvnet

Agent skill for performance-monitor - invoke with $agent-performance-monitor

davila7

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

docker

Extend OpenTelemetry instrumentation when new functionality is added to the MCP Gateway. Use when (1) new operations/functions are added, (2) reviewing code for missing instrumentation, (3) user requests otel/telemetry additions, or (4) working with state-changing operations. Analyzes git diff, suggests instrumentation points following project standards in docs/telemetry/README.md, implements with approval, writes tests, updates documentation, and verifies with debug logging and docker logs.

ruvnet

Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer

sickn33

Use when working with error diagnostics smart debug

davila7

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

sickn33

Use when working with debugging toolkit smart debug

ruvnet

Agent skill for performance-optimizer - invoke with $agent-performance-optimizer

fmind

Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.

incidentfox

Jaeger distributed tracing analysis. Use when investigating request latency, tracing errors across services, finding slow spans, or understanding service dependencies.

benjitaylor

Add Agentation visual feedback toolbar to a Next.js project

mikopbx

Анализ логов Docker контейнера для диагностики проблем и мониторинга здоровья системы. Использовать при отладке ошибок, отслеживании процессов воркеров, исследовании проблем API или мониторинге поведения системы после тестов.

sickn33

Migration monitoring, CDC, and observability infrastructure

comet-ml

TypeScript SDK patterns for Opik. Use when working in sdks/opik-typescript.

ComposioHQ

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

github

Instrument a webapp to send useful telemetry data to Azure App Insights

HoangNguyen0403

Standards for structured logging and observability in Golang.