Skill UI

A comprehensive guide to configuring and maximizing the potential of Claude Code, an advanced agentic coding tool. This resource details best practices, setup templates (CLAUDE.md), advanced prompting techniques (e.g., thinking keywords), and debugging strategies for building reliable, complex software autonomously.

Utilize the Kling AI API to generate high-quality, cinematic videos directly from descriptive text prompts. This advanced tool supports professional modes, camera movement control (pan, tilt, zoom), negative prompting, and synchronized native audio, making it ideal for professionals building robust text-to-video pipelines and creative content generation.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

An advanced AI tool utilizing Meta's Segment Anything 2 (SAM2) model for interactive, pixel-perfect object segmentation. Users can click on any object within a frame or video to automatically generate precise masks. Key features include point/box prompting, video tracking across multiple frames, and seamless integration into Annotation Studio workflows, making it ideal for large-scale data labeling and video analysis pipelines.

This skill provides access to the Anthropic Claude Managed Agent runtime, which is a cloud-native, persistent environment for running complex, long-running, or asynchronous AI tasks. Unlike local WASM agents, the managed runtime provides a full container, persistent filesystem, and robust state management across multiple turns. It supports the entire agent lifecycle: creation, prompting, status inspection, event logging, and mandatory termination.

This skill manages repeated rollouts and recursive prompting by maintaining an append-only decision ledger. It tracks trials, marks, and coherence checks to prevent auto-approval of live trading or destructive actions. Ideal for high-dimensional search, stochastic optimization, and ensemble comparison requiring visible evidence trails.

Restructures any vague build, research, or legal-drafting request into a highly structured IRAC (Issue, Rule, Analysis, Conclusion) prompt. This method optimizes output quality for frontier models by forcing the definition of constraints, underlying rules, required reasoning, and precise deliverables, moving beyond simple prompting.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Learn core capabilities including few-shot learning, chain-of-thought prompting, prompt optimization, template systems, and system prompt design. This skill helps design complex prompts for production applications, optimize consistency, and implement structured reasoning patterns for specialized AI assistants.