Skill UI

A professional-grade 'Living Pipeline' that integrates advanced AI reasoning into the SDLC. It provides autonomous guardrails for system security, cloud cost management (FinOps), and Kubernetes compliance. It audits Linux Kernel patches for memory vulnerabilities, detects cost drifts in Terraform plans, and generates hardened K8s manifests from natural language policies.

A powerful operational companion for database developers. This skill assists with the daily lifecycle of database interactions, including translating natural language requirements into optimized SQL queries, generating database migrations, performing comprehensive schema introspection across multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), and identifying performance bottlenecks through detailed query optimization and index recommendations. It also supports ORM patterns like Prisma and SQLAlchemy.

This skill guides developers on building LLM-powered applications using the Claude API, Anthropic SDKs, or the Agent SDK. It provides comprehensive guidance on language detection, best practices (like default model versions and streaming), and choosing the correct development surface (API call, Workflow, or Agent) based on the use case.

A comprehensive security tool designed to detect prompt injection attacks targeting Large Language Models (LLMs). It employs a multi-layered approach combining regex signature matching, structural anomaly scoring, and advanced DeBERTa transformer classification. It is essential for securing input validation layers in chatbots, AI agents, and RAG pipelines, and for retrospectively auditing LLM interaction logs against OWASP standards.

This comprehensive tool generates high-quality Mermaid diagram code (including flowcharts, sequence diagrams, ER diagrams, etc.) directly from natural language user requirements. It handles complex structured output, mandatory syntax validation using dedicated CLI tools, and detailed visual review to ensure the accuracy of all components and connections.

Use Dart 3 language features—branches, patterns, pattern types, and records—correctly and idiomatically. Apply when writing or refactoring switch statements, if-else chains, creating data classes, choosing records vs classes, destructuring values, or modernizing pre-Dart-3 code.

This skill guides users to discover and install specialized agent capabilities from a vast open marketplace. It handles complex natural language queries like "how do I do X" or "find a skill for Y," ensuring the user receives the most relevant, high-quality, and battle-tested tools for development, testing, or design tasks, followed by secure, managed installation procedures.

This skill acts as a comprehensive Go modernization engineer, scanning entire codebases to update outdated patterns, deprecated APIs, and standard library usage. It utilizes multi-agent orchestration to check language features, testing patterns, and tooling/infra, ensuring the codebase adheres to the latest Go idioms for maximum safety and readability. Ideal for CI/CD pipelines or large-scale code reviews.

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.

A comprehensive guide to tracking the rapidly evolving Go programming ecosystem. It curates official documentation (go.dev, pkg.go.dev), essential newsletters, active communities (r/golang, Slack), influential developers, and top blogs. Use this skill when you need to stay current with Go language updates, discover new libraries, or connect with the development community.

This skill generates highly structured DESIGN.md files, acting as a semantic design system for Google Stitch. It translates complex, anti-generic frontend principles—including typography, color calibration, component behavior, and motion physics—into actionable, natural-language rules. Use it as the single source of truth to guide AI in generating premium, non-clichéd, professional user interfaces.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).