Skill UI

This skill provides comprehensive methods for managing the entire data lifecycle of LLM observability platforms like Langfuse. Functions include exporting detailed traces and scores, configuring retention policies (e.g., automatic data cleanup), handling GDPR subject requests (access and erasure), and anonymizing Personally Identifiable Information (PII) for secure analytics. Essential for maintaining regulatory compliance in AI applications.

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

A comprehensive guide for troubleshooting and resolving Langfuse-related incidents and outages. It provides step-by-step procedures for initial triage, severity assessment, implementing fallback modes, and resolving common issues like missing traces or rate limits, ensuring continuous observability monitoring for LLM applications.

Provides a deep dive and step-by-step scripts for complex Langfuse migrations. Covers comprehensive data export (traces, scores, prompts, datasets) and import processes. This guide is essential for platform transitions, moving between different Langfuse instances, or migrating from observability tools like LangSmith, supporting zero-downtime dual-write patterns.

This skill provides a comprehensive guide to setting up full observability for Lokalise integrations. It covers crucial metrics, traces, and alert configurations, monitoring everything from API response times and rate limit consumption to translation progress and webhook reliability. Ideal for developers building robust monitoring dashboards and alerting systems for localization workflows.

A comprehensive guide to integrating Sentry into continuous integration and continuous deployment (CI/CD) pipelines (e.g., GitHub Actions, GitLab CI, CircleCI). This skill automates complex tasks such as creating releases, uploading source maps for readable stack traces, associating commits, and tracking deployment environments, ensuring rapid and accurate debugging of production errors.

This skill guides the setup of distributed tracing and performance monitoring using Sentry. It covers configuring the SDK (e.g., setting tracesSampleRate) to capture latency, identify performance bottlenecks, and trace requests across the entire application stack. It demonstrates how to create manual spans for business logic and utilizes profiling for deeper performance insights, ensuring comprehensive application observability.

A comprehensive guide to optimizing Sentry's performance monitoring pipeline. Learn how to minimize SDK overhead and event costs while maximizing signal quality. Techniques covered include replacing static sampling rates with dynamic `tracesSampler`, configuring profiling rates, preventing cardinality explosion through proper transaction naming, and implementing custom measurements for deeper business logic visibility. Essential for high-traffic, production applications.

A comprehensive guide to managing Sentry's event volume, quotas, and rate limits. Learn how to prevent 429 Too Many Requests errors and control costs by implementing client-side sampling (sampleRate/tracesSampleRate), using beforeSend filters to drop noisy errors, and understanding various quota types (Errors, Transactions, Replays). Essential for high-volume, cost-sensitive applications.

This guide details how to use the Sentry CLI to manage the full application release lifecycle. Users can create versioned releases (using Semver or SHA), upload source maps to deminify stack traces, and associate specific git commits. This ensures that every error is accurately linked to the exact code version and deployment that caused it, significantly improving debugging and observability in CI/CD pipelines.

A comprehensive guide covering observability patterns for service meshes like Istio and Linkerd. Learn how to implement the three pillars—Metrics, Traces, and Logs—to monitor service health, visualize dependencies, track golden signals (latency, traffic, errors, saturation), and debug microservice communication issues across complex distributed systems.

An expert skill designed for writing, reviewing, and refactoring SwiftUI code for iOS and macOS. It provides deep insights into modern state management (@Observable), optimizing view composition, handling data flow, and improving performance. The skill also assists with interpreting Instruments traces and adhering to Apple's latest Human Interface Guidelines.