Query Ghost production telemetry spans, logs, and traces via Logfire MCP.
npx skills add https://github.com/mrtolkien/GHOST --skill logfire
Please help me install this Agent Skill. Skill: logfire Source: https://github.com/mrtolkien/GHOST/tree/main/.agents/skills/logfire Command: npx skills add https://github.com/mrtolkien/GHOST --skill logfire
Ghost operators need quick, structured access to production telemetry to diagnose live issues without digging through raw logs. Logfire MCP provides centralized querying of spans, logs, and traces to surface actionable insights for debugging and reliability.
Query Ghost production telemetry to identify issues in live deployments and verify fixes.
High-intent search queries and answers about installing and using this skill.
Query Ghost production telemetry by searching across spans, logs, and traces through the Logfire MCP to surface root causes, dashboards, and alerts for live debugging. It executes targeted searches on an Apache DataFusion engine using Postgres-like SQL syntax.
Querying Ghost traces and logs requires an org-scoped token configured with the project name 'ghost'. The service name must be set to 'GHOST' to execute telemetry queries on the underlying Apache DataFusion engine.
Querying traces and spans helps identify production bottlenecks by inspecting trace relationships and filtering logs to surface errors and latency issues within the Ghost environment. This structured approach avoids digging through raw logs manually.
Yes, you can use Postgres-like SQL syntax to filter Ghost telemetry data because the Logfire MCP executes queries on an Apache DataFusion engine. This allows targeted searches across logs, spans, and traces to diagnose production issues.
The best way to monitor Ghost system health is by using dashboards and alerts integrated into the Logfire MCP. These features allow operators to track telemetry, observe spans, and respond to live incidents quickly without parsing raw logs.
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