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.

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.

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.

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 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.

The Codebase Memory MCP provides an accelerated graph-backed system for deep code discovery, architectural analysis, and tracing dependencies. It assists in symbol lookup, impact analysis, and understanding module relationships in large codebases. Users should treat the graph as an accelerator and confirm all conclusions with local source code.

A comprehensive, multi-phase methodology for debugging complex software issues. This guide mandates systematic root cause investigation, starting from symptom collection, code path tracing, hypothesis formulation, and deterministic reproduction. It teaches developers to fix the underlying cause, not just the visible symptoms, ensuring highly robust and minimal changes.

This methodology guides developers to diagnose ambiguous failures, intermittent bugs, or performance regressions before implementing any code changes. It emphasizes separating observed symptoms from inferred causes, tracing inputs and state transitions, and ranking hypotheses based on evidence and cheap falsification value. Use this structured approach to ensure fixes are built upon a proven mechanism.