Skill UI

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.

This tool transforms natural language requirements into high-quality, structured Mermaid diagrams. It supports over 20 diagram types, including Flowcharts, Sequence Diagrams, Class Diagrams, ER Diagrams, and Gantt Charts. The process includes parsing requirements, generating raw Mermaid code, and verifying syntax using industry-standard tools, ensuring accurate and professional visualizations for technical documentation.

This skill acts as a Cognitive Psychologist, diagnosing a target audience's precise stage on the awareness ladder (from unaware to most aware). It applies psychological frameworks (like ELM) to calibrate the optimal messaging, language register, and persuasion strategy. Use it when you need to tailor marketing content, sales pitches, or educational materials based on the audience's current knowledge and readiness to buy, ensuring the message resonates and minimizes resistance.

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.

This utility compresses verbose natural language notes, memories, and documents (like markdown files) into a highly dense format. By eliminating filler words, redundant phrases, and grammatical fluff, it significantly reduces the token count for large inputs. Crucially, it strictly preserves all technical content, including code blocks, inline code, URLs, file paths, and commands, ensuring technical accuracy while boosting AI processing efficiency.

This skill covers the advanced language features introduced in Dart 3, including Pattern Matching, Records, sealed classes, and enhanced control flow structures. Learn how to use `if-case` and switch expressions to write safer, more concise, and idiomatic Dart code, and how to manage complex data shapes using records and patterns.

A comprehensive skill for senior Go security engineers, providing structured modes (Review, Audit, Coding) to detect vulnerabilities in Go applications. It implements deep security thinking using principles like Defense in Depth, STRIDE, and DREAD scoring. Use this when writing, reviewing, or auditing Go code for common risks including injection, crypto misuse, authentication flaws, and concurrency issues.

Curated guide providing Go resources, newsletters, communities, blogs, and key developers to follow so you can discover libraries, track releases, and learn the language consistently.

This skill transforms written content, such as articles and reports, into engaging, two-host conversational podcast audio. It first structures the content into a detailed JSON script (dialogue format) and then uses advanced Text-to-Speech synthesis to generate the final MP3 file. Supports both English and Chinese languages, making content accessible for audio-only listening.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

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++).