Skill UI

Implement comprehensive observability for Apollo.io API integrations. This skill provides structured instrumentation covering Prometheus metrics (request count, latency, rate limits, credits used), structured logging with PII redaction, and OpenTelemetry tracing. It ensures reliable tracking of API health, performance, and resource consumption.

This SDK provides comprehensive support for automatically instrumenting Node.js applications to collect observability data. It integrates distributed tracing, metrics, and logs using the OpenTelemetry standard, allowing seamless data export and monitoring within Azure Monitor. It supports various resource types and connection methods.

This skill provides a comprehensive solution for implementing observability for Customer.io integrations. It covers setting up Prometheus metrics (latency, request counts, bounces), structured JSON logging with PII redaction, and OpenTelemetry tracing. Use it when building robust, production-grade monitoring dashboards and alerting systems for Customer.io APIs.

Implements a comprehensive observability stack for Deepgram integrations. It utilizes Prometheus metrics for tracking request rates, latency, and costs, OpenTelemetry for distributed tracing, Pino for structured logging, and AlertManager rules for proactive alerting. Essential for production monitoring, dashboard setup, and performance analysis of Deepgram API usage.

Performs security-focused differential review of PRs, commits, and diffs, adapting depth to repo size, tracing git history, estimating blast radius, checking coverage, and producing detailed markdown reports to stop regressions.

This skill acts as an expert guide for setting up comprehensive debugging environments, implementing distributed tracing solutions, and establishing robust diagnostic workflows. It is ideal for diagnosing complex, multi-service issues in development or production environments by standardizing logging, tracking correlation IDs, and ensuring end-to-end observability.

This skill provides comprehensive guidance on implementing distributed tracing using industry standards like OpenTelemetry and tools such as Jaeger/Tempo. It enables developers to monitor the end-to-end lifecycle of a request across multiple microservices. Use this skill to debug latency issues, visualize complex service dependencies, pinpoint performance bottlenecks, and accurately trace error propagation in highly distributed systems.

This skill provides expert capabilities for diagnosing and resolving complex system failures in distributed environments. It is designed for investigating production incidents, performing deep root-cause analysis using observability tools (logging, tracing), and proposing robust, preventive fixes. Use it to significantly enhance system stability and reliability.

A comprehensive toolkit designed to debug and troubleshoot Evernote API integrations. It provides deep logging of requests, responses, and timings. Features include an instrumented client wrapper for zero-config tracing, ENML content validation with auto-fix capabilities, and token inspection utilities to diagnose connectivity issues and quota limits.

Provides a comprehensive framework for implementing observability in Evernote integrations. It covers metrics collection (Prometheus), structured JSON logging, distributed tracing (OpenTelemetry), and defining health/readiness endpoints. Use this when setting up production monitoring, alerting, and performance tracking for mission-critical Evernote APIs.

A comprehensive guide for Site Reliability Engineers (SRE) covering the entire incident lifecycle. It provides structured methodologies for initial severity assessment, establishing an incident command structure, advanced observability-driven investigation (tracing, metrics, logs), systematic troubleshooting, and crucial post-incident root cause analysis (blameless post-mortems). Ideal for managing complex, large-scale system outages.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.