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.

SGLang is a high-performance serving framework for LLMs and VLMs that uses RadixAttention prefix caching to deliver structured outputs (JSON/regex/grammar) and agentic workflows with function calls, providing up to 5× faster inference than vLLM across multi-GPU production deployments.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.

A comprehensive expert for deploying and scaling AI agents using AWS Bedrock AgentCore. This guide covers the full lifecycle of agent management, including service selection (Gateway, Runtime, Memory, Identity, etc.), deployment patterns, credential management, and advanced integration workflows required for enterprise-scale AI solutions.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

This advanced guide covers core Anthropic workflow patterns, specifically focusing on tool use (function calling) and building complex, agentic loops. It provides detailed instructions on defining tools using JSON Schema, executing tool calls, and managing the iterative process required for building sophisticated, AI-powered agents using the Anthropic API.

The Ejentum Reasoning Harness is a comprehensive library of cognitive operations designed to guide complex AI agents through critical thinking tasks. It exposes four specialized harnesses (reasoning, code, anti-deception, memory) that mitigate common failure modes like hallucination and reasoning decay. Instead of a fixed answer, the agent ingests a detailed, executable scaffold—including failure patterns, procedures, and falsification tests—to ensure robust, self-verified output.

Implements a comprehensive defense-in-depth framework for autonomous AI agents that interact with external tools. It mitigates risks like prompt injection or tool misuse by enforcing strict tool allowlisting, binding tools to least-privilege identities, requiring human-in-the-loop approval for high-impact actions, and utilizing policy enforcement (like NeMo Guardrails) to bound the blast radius and ensure compliance.

This comprehensive skill provides an index of official blueprints and implementation guides for building full-stack, AI-powered applications using Weaviate. It covers advanced patterns such as Retrieval-Augmented Generation (RAG), agentic workflows, data exploration, and multimodal document search, guiding users through best practices for setup, clients, and frontends.

This skill wraps the `getCompactContext` functionality to efficiently retrieve a compacted ReasoningBank context, specifically optimized for cost-analysis queries. It generates a detailed report summarizing critical metrics such as tokens saved, memories retrieved, and agentic-flow availability. It is essential for monitoring and optimizing resource usage in complex AI workflows, providing deep visibility into the efficiency and cost of the context retrieval mechanism.

This tool enables the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static agents, DAA agents possess the ability to learn, adapt their internal behavior based on real-time performance metrics, and continuously evolve through interactions. It facilitates knowledge sharing among multiple agents, ensuring systemic, continuous improvement in complex AI systems. Ideal for scenarios requiring high adaptability and evolving intelligence.