👁️Agent Observability Skills
Browse the best Agent Observability skills for Claude Code, Codex, Cursor and other AI agents.
260 skills · Page 1 of 11
langsmith-observability
★ 481LLM 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.
reflect-on-work
Pattern for producing quality reflections after completing work. Required for all agent outputs.
agent-mlops
Production deployment and operationalization of AI agents on Databricks. Use when deploying agents to Model Serving, setting up MLflow logging and tracing for agents, implementing Agent Evaluation frameworks, monitoring agent performance in production, managing agent versions and rollbacks, optimizing agent costs and latency, or establishing CI/CD pipelines for agents. Covers MLflow integration patterns, evaluation best practices, Model Serving configuration, and production monitoring strategies.
observability-logging
Automatically applies when adding logging and observability. Ensures structured logging, OpenTelemetry tracing, LLM-specific metrics (tokens, cost, latency), and proper log correlation.
observability-patterns
Comprehensive observability setup patterns for Google ADK agents including logging configuration, Cloud Trace integration, BigQuery Agent Analytics, and third-party observability tools (AgentOps, Phoenix, Weave). Use when implementing monitoring, debugging agent behavior, analyzing agent performance, setting up tracing, or when user mentions observability, logging, tracing, BigQuery analytics, AgentOps, Phoenix, Arize, or Weave.
phoenix-observability
★ 15.4kOpen-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.
langsmith-observability
★ 15.4kLLM 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.
performance-analysis
★ 9.9kComprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
background-agent-pings
★ 2.2kBackground Agent Pings
braintrust-tracing
★ 2.2kBraintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging
mot
★ 2.2kSystem health check (MOT) for skills, agents, hooks, and memory
trace-claude-code
★ 1.3kAutomatically trace Claude Code conversations to Braintrust for observability.Captures sessions, conversation turns, and tool calls as hierarchical traces.
langchain-observability
★ 939Set up comprehensive observability for LangChain integrations.Use when implementing monitoring, setting up dashboards,or configuring alerting for LangChain application health.Trigger with phrases like "langchain monitoring", "langchain metrics","langchain observability", "langchain tracing", "langchain alerts".
langchain-cost-tuning
★ 939Optimize LangChain API costs and token usage.Use when reducing LLM API expenses, implementing cost controls,or optimizing token consumption in production.Trigger with phrases like "langchain cost", "langchain tokens","reduce langchain cost", "langchain billing", "langchain budget".
lindy-observability
★ 939Implement observability for Lindy AI integrations.Use when setting up monitoring, logging, tracing,or building dashboards for Lindy operations.Trigger with phrases like "lindy monitoring", "lindy observability","lindy metrics", "lindy logging", "lindy tracing".
langchain-webhooks-events
★ 939Implement LangChain callback and event handling for webhooks.Use when integrating with external systems, implementing streaming,or building event-driven LangChain applications.Trigger with phrases like "langchain callbacks", "langchain webhooks","langchain events", "langchain streaming", "callback handler".
lindy-debug-bundle
★ 939Comprehensive debugging toolkit for Lindy AI agents.Use when investigating complex issues, collecting diagnostics,or preparing support tickets.Trigger with phrases like "lindy debug", "lindy diagnostics","lindy support bundle", "investigate lindy issue".
perplexity-observability
★ 939Set up comprehensive observability for Perplexity integrations with metrics, traces, and alerts.Use when implementing monitoring for Perplexity operations, setting up dashboards,or configuring alerting for Perplexity integration health.Trigger with phrases like "perplexity monitoring", "perplexity metrics","perplexity observability", "monitor perplexity", "perplexity alerts", "perplexity tracing".
windsurf-observability
★ 939Set up comprehensive observability for Windsurf integrations with metrics, traces, and alerts.Use when implementing monitoring for Windsurf operations, setting up dashboards,or configuring alerting for Windsurf integration health.Trigger with phrases like "windsurf monitoring", "windsurf metrics","windsurf observability", "monitor windsurf", "windsurf alerts", "windsurf tracing".
retellai-observability
★ 920Set up comprehensive observability for Retell AI integrations with metrics, traces, and alerts.Use when implementing monitoring for Retell AI operations, setting up dashboards,or configuring alerting for Retell AI integration health.Trigger with phrases like "retellai monitoring", "retellai metrics","retellai observability", "monitor retellai", "retellai alerts", "retellai tracing".
openrouter-usage-analytics
★ 878Track and analyze OpenRouter usage patterns. Use when optimizing costs or understanding usage. Trigger with phrases like 'openrouter analytics', 'openrouter usage', 'openrouter metrics', 'track openrouter'.
massgen-log-analyzer
★ 681Run MassGen experiments and analyze logs using automation mode, logfire tracing, and SQL queries. Use this skill for performance analysis, debugging agent behavior, evaluating coordination patterns, and improving the logging structure, or whenever an ANALYSIS_REPORT.md is needed in a log directory.
phoenix-observability
★ 481Open-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.
session-reflection-analysis
★ 277Use when asked to reflect on how the session went