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Found 16 Skills
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace URL (e.g. /llm-observability/traces/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs.
Write raw ClickHouse SQL for a SigNoz dashboard panel — timeseries, value, or table widgets that the builder UI cannot express (custom joins, window functions, regex extraction over log bodies, aggregations beyond builder syntax). Trigger when the user explicitly asks for a "ClickHouse query", a "raw SQL panel", a "custom SQL widget", or describes a SigNoz dashboard panel whose query needs SQL the builder cannot produce. Anchored to dashboard-panel SQL specifically. For ad-hoc data exploration that does not need to land in a panel, use `signoz-generating-queries` instead.
Generate, write, or run an ad-hoc query against SigNoz observability data — metrics, logs, traces, or exceptions — without wrapping it in a dashboard panel or alert. Make sure to use this skill whenever the user asks "show me error rates", "query logs for timeout errors", "what's the p99 latency for the cart service", "how many requests hit the payment endpoint", "find slow traces", "errors in the last hour", or otherwise asks an exploratory question that needs live observability data — even if they don't say "query" or "search" explicitly.
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you want to truly understand what your agent is doing in production.