Skill UI

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.

A specialized downstream skill for analyzing security vulnerabilities in complex agentic systems. This tool is designed for red teaming and penetration testing, focusing on detecting prompt injection, retrieval poisoning, memory contamination, planner drift, and tool boundary abuse within multi-step LLM chains. Use when proving how untrusted inputs compromise system integrity.

A skill that models AI educator Baoyu's approach to creating accessible AI content, systematic prompt engineering teaching (Level 1-10), tool reviews, open-source sharing, and daily knowledge updates. Ideal for writing tutorials, translating tech articles, and building AI knowledge bases without hype or anxiety.

This skill enables systematic improvement of existing AI agents through performance analysis, prompt engineering, and continuous iteration. It involves analyzing failure modes, collecting baseline metrics, and applying optimization techniques like chain-of-thought enhancement and role refinement to enhance reliability and task success rates.

This toolkit provides six expert-level workflows for AI engineering, including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior partners by offering structured methodologies, quantitative scoring, and decision frameworks. Suitable for optimizing LLM systems, designing secure agents, and planning product strategy before development.

This skill converts human-written CLAUDE.md files into AI-native structured-label formats. It proposes and evaluates rewrites while preserving rule meaning, ensuring benefits are evidenced. Ideal for optimizing LLM system prompts, reducing token usage, and migrating rules across different AI tools like Claude, Codex, and Gemini.

This skill covers the core concepts of context engineering for language models, including system prompts, tool definitions, retrieved documents, message history, and tool outputs. It explains how attention mechanisms work, context window constraints, and strategies for optimizing context usage to reduce costs and improve agent performance.

This skill involves testing AI agents for direct prompt injection vulnerabilities where user messages attempt to override system instructions. It focuses on role confusion and instruction hijacking without external content, ensuring agents maintain security boundaries against direct manipulation attempts.

This skill focuses on identifying security vulnerabilities in Large Language Model (LLM) and AI features. It covers prompt injection, system prompt leakage, cross-tenant access, and data exfiltration techniques. Users must verify findings against false positives using reproducibility checks and out-of-band callbacks before reporting.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Conduct authorized security assessments for LLM applications and AI agents. Covers prompt injection, tool abuse, RAG exposure, memory poisoning, and system prompt extraction following OWASP LLM/ASI Top 10 guidelines. Includes red-teaming, vulnerability mapping, and compliance engineering for AI systems to ensure safety and robustness.

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.