Skill UI

This skill provides a comprehensive guide to designing and developing autonomous AI agents for code plugins. It covers the required file structure (YAML frontmatter), best practices for defining agent roles, designing robust system prompts, and setting up clear triggering conditions. Use this when you need to conceptualize, structure, or refine an agent's capabilities to handle complex, multi-step tasks autonomously.

Guides how to author Claude Code slash commands using Markdown prompts, YAML frontmatter, dynamic arguments, Bash execution, and best-practice organization for reusable workflows and interactive automation.

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.

A comprehensive guide to structuring effective custom prompts for AI coding assistants like Cursor. Learn the four components of prompt anatomy (Context, Task, Constraints, Format), and utilize reusable templates for complex tasks such as feature implementation, bug fixing, code review, and test generation. Master advanced techniques like Chain of Thought and Few-Shot examples to ensure high-quality, consistent code output.

Guides creation of Cursor-specific subagents with isolated contexts, structured prompts, and frontmatter so multi-step workflows, verifiers, or specialists follow Cursor’s directories and delegation rules.

DSPy lets you declaratively program complex language model systems, assemble modular RAG/agent pipelines, and optimize prompts and modules automatically for reliable LM-driven apps.

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

Generates Excalidraw diagrams from natural-language prompts, turning requests such as “make a flowchart” or “draw a system architecture” into ready-to-open .excalidraw JSON files for flows, relationships, mind maps, and architectures.

Generates pivot tables and charts from raw data using natural-language prompts, letting you summarize sales, cross-tabulate dimensions, apply filters, and add visuals without manual Excel setup.

This skill enables the generation of high-quality images and videos using advanced fal.ai AI models. It is designed for creative professionals, artists, and developers who require powerful, generative visual content. Use this when your task involves transforming text prompts into sophisticated visual media, making it ideal for concept art, multimedia prototyping, or advanced content creation.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

Leverage the Gamma API to rapidly generate diverse, professional content formats, including interactive presentations, detailed documents, and modern webpages. This skill supports comprehensive parameter control, allowing users to define content, select specific themes, dictate image styles, control text density, and choose output formats (PDF, PPTX, etc.). Ideal for content creators who need to transform raw text prompts into polished, multi-media assets across various professional use cases.