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Found 311 Skills
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.
Senior Site Reliability Engineer & Debug Architect. Expert in AI-assisted observability, distributed tracing, and autonomous incident remediation in 2026.
nginx C module debugging guidelines based on the official nginx development guide. This skill should be used when debugging nginx C module crashes, memory bugs, request flow issues, or production problems. Triggers on tasks involving segfault analysis, coredump debugging, GDB inspection, memory leak detection, request phase tracing, AddressSanitizer setup, or nginx module troubleshooting.
Implement observability for Evernote integrations. Use when setting up monitoring, logging, tracing, or alerting for Evernote applications. Trigger with phrases like "evernote monitoring", "evernote logging", "evernote metrics", "evernote observability".
Implement comprehensive observability for Guidewire InsuranceSuite including logging, metrics, tracing, and alerting. Trigger with phrases like "guidewire monitoring", "logging guidewire", "metrics", "observability", "alerting", "dashboards guidewire".
Use when coordinating handoffs between workflows (e.g., skill-editor to programming-pm). Provides universal handoff schema v3.0, validation rules, distributed tracing conventions, and workflow discovery via frontmatter metadata.
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Use when an existing agent already works without Prefactor and you need to add tracing for runs, llm calls, tool calls, and failures with minimal behavior changes.
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTTP, Tornado, and more.
Guidelines for structured logging, distributed tracing, and debugging patterns across languages. Covers logging best practices, observability, security considerations, and performance analysis.
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.