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Found 224 Skills
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
Make application behavior visible to coding agents by exposing structured logs and telemetry. Use when asked to "add telemetry", "make logs accessible to agents", "add observability", "debug with logs", or when an agent needs to understand runtime behavior but has no way to query logs. Also use when debugging is difficult because there are no structured logs, when agent docs (CLAUDE.md, AGENTS.md) lack instructions for querying application logs, or when setting up logging infrastructure for a new or existing web application.
Implement distributed tracing using logs, including trace context propagation, span logging, correlation IDs, and OpenTelemetry integration for observability
Guidelines for structured logging, distributed tracing, and debugging patterns across languages. Covers logging best practices, observability, security considerations, and performance analysis.
Envoy Gateway production deployment — deployment modes, performance tuning, observability, operational guidance
Instrument web applications to send telemetry data to Azure Application Insights for observability and monitoring. USE FOR: instrument app with app insights, add appinsights instrumentation, configure application insights, set up telemetry monitoring, enable app insights auto-instrumentation, add observability to azure web app, instrument webapp to send data to app insights, configure telemetry for app service. DO NOT USE FOR: non-Azure monitoring (use CloudWatch for AWS, Datadog for third-party), log analysis (use azure-kusto), cost monitoring (use azure-cost-optimization), security monitoring (use azure-security).
Azure Observability Services including Azure Monitor, Application Insights, Log Analytics, Alerts, and Workbooks. Provides metrics, APM, distributed tracing, KQL queries, and interactive reports.
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.
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.