Skill UI

Implement a comprehensive observability stack for Webflow Data API v2 integrations. This skill provides structured code using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging (pino) to monitor API call frequency, latency, error rates, and rate limit usage. Ideal for developers building robust, production-grade Webflow-connected SaaS applications that require deep performance insights and reliable alerting.

This extension provides comprehensive distributed tracing capabilities for the KubeSphere Observability Platform, adhering to the OpenTelemetry standard. It guides users through the entire lifecycle, including component installation (Generator, Collector, Agent), configuration, and querying complex tracing data, spans, and service graphs. It is essential for monitoring microservices and understanding complex system interactions.

An expert AI specializing in Slang shader programming for high-performance graphics and compute applications. This model excels at writing, reviewing, refactoring, and optimizing production-grade code for various graphics stages (vertex, fragment, compute, ray tracing, tessellation). It ensures cross-platform compatibility across major APIs (D3D12, Vulkan, Metal, OpenGL) and supports advanced concepts like generics, parameter blocks, and automatic differentiation. Use this skill when integrating shaders into C++ engines or requiring deep knowledge of low-level GPU pipelines.

Provides expert guidance for connecting to Power BI Desktop's local tabular model using the Tabular Object Model (TOM) and ADOMD.NET via PowerShell. This skill covers model enumeration, executing DAX queries, metadata modification (tables, measures, relationships), query tracing, and model refreshing. It is specifically designed for managing the underlying data model structure when external tools like Tabular Editor CLI are unavailable.

This guide establishes best practices for code instrumentation and system observability. Learn how to implement structured logging, metrics (RED), and distributed tracing to make production behavior fully diagnosable. It is essential for any feature deployed live, ensuring rapid incident response and deep system understanding.

An adversarial Quality Assurance (QA) pass designed to deep-dive into any codebase for logic errors, security flaws, and functional bugs. Instead of confirming that code 'looks right,' this skill actively attempts to break the code by simulating hostile inputs, tracing critical data paths, and checking for subtle, real-world correctness issues. Essential for high-stakes, critical code reviews.

A specialized tool for blockchain forensics, designed to trace the movement of ransomware payments across public blockchains (like Bitcoin). By leveraging APIs from services like Chainalysis and WalletExplorer, it identifies wallet clusters, tracks funds through mixers and tumblers, and maps fund flow from the victim to final exchange deposits. Essential for law enforcement attribution and threat intelligence gathering.

A comprehensive workflow and deep technical guide for analyzing malicious Linux ELF binaries. Covers static analysis (reading ELF headers, checking entropy, identifying strings), dynamic tracing (monitoring system calls and library usage with strace/ltrace), and reverse engineering (using GDB) of threats like ransomware, botnets, and rootkits targeting Linux servers and containers (x86_64, ARM).

Finds over-permissive RBAC roles and service-account token abuse paths in a Kubernetes cluster using kubectl auth can-i, rbac-police, kubectl-who-can, and rakkess, tracing which subjects can escalate toward cluster-admin. Use when reviewing who can escalate privileges in a cluster, hunting exploitable RoleBindings during an authorized review, or validating least privilege after an RBAC change.

This skill performs a deep comparison of two code versions to verify semantic equivalence, going beyond simple syntax diffing. It uses semi-formal tracing techniques to trace both versions against shared specifications and boundary conditions (e.g., null, zero, max/min). Ideal for confirming if a refactor, rewrite, or migration has unintentionally altered the code's behavior, answering the question: "Did I break anything?"

This sophisticated skill is designed to locate the root cause of a confirmed software failure. It performs a systematic, semi-formal tracing process: first tracing backward from the observed failure point (using provided stack traces, assertions, or error messages) to identify the original premise, and then tracing forward to confirm the symptom. It outputs a highly structured report detailing the divergence, premises, and remedy.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.