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langfuse-core-workflow-a

Execute Langfuse primary workflow: Tracing LLM calls and spans. Use when implementing LLM tracing, building traced AI features, or adding observability to existing LLM applications. Trigger with phras

langfuse-data-handling

Manage Langfuse data export, retention, and compliance requirements. Use when exporting trace data, configuring retention policies, or implementing data compliance for LLM observability. Trigger with

langfuse-incident-runbook

Troubleshoot and respond to Langfuse-related incidents and outages. Use when experiencing Langfuse outages, debugging production issues, or responding to LLM observability incidents. Trigger with phra

langfuse-install-auth

Install and configure Langfuse SDK authentication for LLM observability. Use when setting up a new Langfuse integration, configuring API keys, or initializing Langfuse tracing in your project. Trigger

langfuse-migration-deep-dive

Execute complex Langfuse migrations including data migration and platform changes. Use when migrating from other observability platforms, moving between Langfuse instances, or performing major infrast

langfuse-observability

Set up comprehensive observability for Langfuse with metrics, dashboards, and alerts. Use when implementing monitoring for LLM operations, setting up dashboards, or configuring alerting for Langfuse i

langfuse-rate-limits

Implement Langfuse rate limiting, batching, and backoff patterns. Use when handling rate limit errors, optimizing trace ingestion, or managing high-volume LLM observability workloads. Trigger with phr

langfuse-reference-architecture

Production-grade Langfuse architecture patterns and best practices. Use when designing LLM observability infrastructure, planning Langfuse deployment, or implementing enterprise-grade tracing architec

langfuse-sdk-patterns

Langfuse SDK best practices, patterns, and idiomatic usage. Use when learning Langfuse SDK patterns, implementing proper tracing, or following best practices for LLM observability. Trigger with phrase

lindy-observability

Monitor Lindy AI agent health, task success rates, and credit consumption. Use when setting up monitoring, building dashboards, configuring alerts, or tracking agent performance over time. Trigger wit

linear-observability

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, dashboards, or configuring alerts for Linear API usage. Trigger: "linear monitoring", "line

maintainx-observability

Implement comprehensive observability for MaintainX integrations. Use when setting up monitoring, logging, tracing, and alerting for MaintainX API integrations. Trigger with phrases like "maintainx mo

mistral-observability

Set up comprehensive observability for Mistral AI with metrics, traces, and alerts. Use when implementing monitoring for Mistral AI operations, setting up dashboards, or configuring alerting for integ

monitoring-cpu-usage

Monitor this skill enables AI assistant to monitor and analyze cpu usage patterns within applications. it helps identify cpu hotspots, analyze algorithmic complexity, and detect blocking operations. u

monitoring-database-health

Monitor use when you need to work with monitoring and observability. This skill provides health monitoring and alerting with comprehensive guidance and automation. Trigger with phrases like "monitor s

monitoring-database-transactions

palantir-observability

Set up observability for Palantir Foundry integrations with metrics, logging, and alerts. Use when implementing monitoring for Foundry API calls, setting up dashboards, or configuring alerting for Fou

plane

Plane is a team behavior observatory — synthesize Plane API data into observations about how teams actually behave under pressure, not just ticket state. Five compound commands surface cycle velocity

replit-observability

Monitor Replit deployments with health checks, uptime tracking, resource usage, and alerting. Use when setting up monitoring for Replit apps, building health dashboards, or configuring alerting for de

sentry-error-capture

Implement advanced error capture and context enrichment with Sentry. Use when adding captureException/captureMessage calls, enriching errors with user context, tags, breadcrumbs, or custom fingerprint

sentry-incident-runbook

Execute incident response procedures using Sentry error monitoring. Use when investigating production outages, triaging error spikes, classifying incident severity, or building postmortem reports from

sentry-observability

Integrate Sentry with your observability stack — logging, metrics, APM, and dashboards. Use when connecting Sentry to winston/pino/structlog, correlating errors with business metrics, deciding between

sentry-rate-limits

Manage Sentry rate limits, quotas, and event volume optimization. Use when hitting 429 errors, tuning sampleRate/tracesSampleRate, filtering noisy browser errors with beforeSend, configuring inbound d

snowflake-data-quality-sentinel

Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring