Skill UI

Behavioral rules for LLMs writing or reviewing code, emphasizing caution, simplicity, surgical edits, and goal-driven execution to avoid common mistakes during implementation tasks, bug fixes, or refactors.

Fuzzing Dictionary supplies domain-specific tokens to guide fuzzers when targeting parsers, protocols, configs, or file formats so they reach deeper logic; include magic bytes, headers, keywords, and combine generation from LLMs, headers, or binaries before passing via the fuzzer flag.

Guide for building high-quality MCP servers that let LLMs interact with external APIs via workflow-focused tools, SDKs, validation, and error handling in Python or TypeScript.

Guides the creation of MCP servers that let LLMs interface with external APIs or services, covering research, planning, and implementation in TypeScript, Python, or Microsoft stacks along with naming, transport, and tooling best practices.

A comprehensive guide for developing custom external web service APIs for the Moodle Learning Management System (LMS). It covers Moodle's required API framework, coding standards, and the essential three-method pattern (execute_parameters, execute, execute_returns) necessary for integrating custom functionality, such as course management, quiz operations, and reporting, with external applications.

Serve production-grade LLM APIs with vLLM, leveraging PagedAttention, continuous batching, quantization, and tensor parallelism to maximize throughput while keeping latency, GPU usage, and scalability under control for OpenAI-compatible deployments.

MarkItDown converts PDFs, DOCX, PPTX, XLSX, HTML, JSON, XML, archives, YouTube links, images via OCR, and audio via transcription into clean Markdown, providing token-efficient text ready for LLMs and documentation workflows.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This guide assists developers in designing and architecting Model Context Protocol (MCP) servers that integrate deeply with LLMs like Claude. It is not a coding skill, but a comprehensive consultation process that guides users through use case interrogation, determines the optimal deployment model (e.g., Remote HTTP, MCPB), and selects the appropriate tool-design pattern for seamless, robust integration.

This tool meticulously measures whether AI coding agents adhere to predefined skills, rules, or architectural guidelines. It automatically generates expected behavioral sequences and runs scenarios with varying prompt strictness. It deeply analyzes tool call traces, classifies behaviors using LLMs, and generates detailed compliance reports, ensuring robust quality control and adherence to defined workflows.

Covers every key Golang documentation surface—godoc comments, README, CONTRIBUTING, CHANGELOG, Example tests, Go Playground demos, API docs, llms.txt, and doc-site guidance—so you can write, review, and maintain discoverable docs for both libraries and CLIs.

Behavioral guidelines for writing, reviewing, and refactoring code with LLMs, emphasizing minimalism, clear assumptions, surgical edits, and verifiable success criteria to avoid common machine-assisted coding mistakes.