Skill UI

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

Automate core Agile workflows within Linear. This skill allows users to manage the entire project lifecycle, including creating and updating projects, setting milestones, assigning issues, and managing time-boxed cycles (sprints). It also includes advanced functionalities like calculating cycle metrics and rolling over unfinished tasks, significantly streamlining product development and planning processes.

A comprehensive guide and implementation using TypeScript to establish production-grade observability for Linear API integrations. This includes defining Prometheus metrics (tracking latency, error rates, rate limits, and webhooks), implementing structured logging (using pino), and setting up robust health check endpoints. It ensures full visibility into API performance and system health, critical for mission-critical SaaS applications.

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

This comprehensive checklist guides developers and DevOps engineers through the necessary steps for deploying MaintainX integrations to a production environment. It covers crucial areas such as authentication security, error resilience (retry/circuit breaking), data integrity (idempotency, pagination), observability setup (logging/metrics), and modern deployment best practices, ensuring a stable and reliable go-live process.

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

This skill guides the implementation of comprehensive, real-time monitoring for APIs. It covers collecting key metrics (latency, error rates, request rate), setting up health and readiness endpoints, building detailed Grafana dashboards, and implementing SLO-based alerting (following the RED method). Use this when you need to ensure API reliability, high availability, and performance in a production environment.

Monitors PostgreSQL and MySQL health with real-time metrics, predictive alerts, and automated remediation, letting you preempt bottlenecks and respond to production issues quickly.

Designs comprehensive, production-ready observability strategies by integrating metrics, logs, and traces. This tool guides the setup of SLI/SLO frameworks, optimizes noisy alerting configurations, and generates detailed dashboards, ensuring deep visibility into service health and performance.

A comprehensive toolkit for implementing production-grade observability in Obsidian plugins. It provides structured logging with log levels and history, a robust metrics collector (supporting counters, gauges, and timers for performance tracking), and an error tracker with deduplication. Users can build a real-time debug panel to monitor plugin performance, diagnose issues, and set up comprehensive error reporting.

Provides a comprehensive monitoring solution for the Perplexity Sonar API. It tracks critical performance metrics, including latency (p95), cost per query, error rates, and citation quality. This skill is essential for implementing production monitoring dashboards, configuring proactive alerts (e.g., high latency, zero citations), and ensuring the API's stability and high-quality output in real-world applications.

Read-only assistant that inspects Sentry issues, summarizes recent production errors, and pulls health metrics via the Sentry API when users ask for error reports or recent crashes.