Skill UI

A comprehensive skill for performing deep security audits and writing secure code in Go. It guides developers through identifying vulnerabilities like SQL injection, XSS, path traversal, and crypto misuse. The framework incorporates advanced techniques such as STRIDE/DREAD threat modeling, data flow tracing, and multi-stage review modes (Audit, Review, Coding), ensuring defense in depth for robust Go applications.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

Instrument LLM applications with OpenInference semantic conventions in Phoenix. Covers setup, auto and manual instrumentation, 9 span types, session tracking, production batching and PII masking for Python and TypeScript.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

This technique is used when the required answer in a case query is not directly stated but must be deduced by following a sequence of connected roles, people, or events. It involves mapping complex relationships, such as Victim -> Defendant -> Lawyer -> Witness, to trace indirect connections and reach the final piece of information. Ideal for advanced investigative research and deep knowledge graph traversal.

This technique allows users to locate game developers or projects indirectly by searching for highly distinctive biographical details. Instead of relying on game databases, it leverages unique phrases, historical events, or personal lifestyle details found in interviews, blogs, or conference talks. It is invaluable when the developer's identity is obscured, requiring deep research using exact-match queries on obscure information.

This service provides a centralized, common API server backend for the entire KubeSphere WizTelemetry Observability Platform. It unifies backend functionalities for core observability components including Logging, Auditing, Events, Tracing, and Notifications, enabling global monitoring and streamlined platform management within Kubernetes environments. Use it when installing or configuring the core telemetry platform.

Provides advanced search strategies and domain knowledge for football queries that fall outside the major European leagues (e.g., Premier League, La Liga). This guide covers critical contexts in African leagues (CAF, PSL), South Asian leagues (I-League, ISL), Russian football, and tracing players' origins, ensuring comprehensive and non-biased research on global football history and facts.

A comprehensive guide to implementing observability for Klaviyo API integrations. This solution uses Prometheus metrics (counters, histograms, gauges) to track API volume, latency, and errors, combined with OpenTelemetry for distributed tracing and structured logging. Ideal for setting up robust monitoring dashboards and critical alerting systems for marketing data pipelines.

This guide details how to implement comprehensive observability for various Adobe API integrations (e.g., Firefly, PDF Services). It covers the four pillars of observability: structured metrics using Prometheus, distributed tracing via OpenTelemetry, structured JSON logging, and defining critical alert rules. This ensures deep visibility into API performance, latency, and error rates for robust production monitoring.

This skill provides a comprehensive guide to instrumenting Algolia search services for enterprise-grade observability. It integrates core monitoring patterns, including Prometheus metrics (latency, error rates, request counts), OpenTelemetry distributed tracing, and structured logging. Use this to achieve deep insights into search performance, user experience, and system health in production environments.

Implement comprehensive observability for Canva Connect API integrations using industry best practices. This guide covers gathering critical metrics (latency, error rates, rate limits) with Prometheus, implementing distributed tracing with OpenTelemetry, and setting up robust alert rules. Essential for monitoring API health, optimizing performance, and ensuring system reliability in SaaS environments.