Skill UI

This example demonstrates unsafe or malicious review data inputs, specifically designed for calibration and security testing. It simulates prompt injection attacks and must only be used in controlled environments to validate secure data processing and system robustness.

A structured tool for running A/B tests on marketing content variations. It allows users to compare different approaches—such as headline styles, CTA phrasing, or overall content strategies—by logging variants and evaluating them against technical quality scores (e.g., hallucination, readability). The system provides a comprehensive analysis, identifying a statistically grounded winner and offering actionable recommendations for content improvement.

A specialized downstream skill for CTF-sandbox workflows. Used to analyze complex security challenges driven by AI agent interactions, prompt injection, or infrastructure vulnerabilities. Focuses on tracing control chains involving untrusted input, tool side effects, deployment drift, container runtime issues, and CI/CD supply chain provenance. Ideal for rigorous vulnerability assessment in modern, distributed systems.

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 comprehensive framework for conducting authorized security assessments on Large Language Models (LLMs) and AI agents. It covers critical vulnerabilities including advanced prompt injection (OWASP Top 10), tool abuse, RAG data poisoning, memory manipulation, and system prompt extraction. Ideal for red teaming, security auditing, and ensuring the robustness of AI systems in production environments.

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 attention mechanisms, context windows, and progressive disclosure principles to help optimize agent performance and reduce token costs during inference.

This skill covers persistent memory systems for Large Language Model (LLM) conversations. It includes implementing short-term, long-term, and entity-based memory to enhance context retention across sessions. Features include memory consolidation, retrieval strategies, and integrating memory into prompts. Suitable for building conversational AI applications using tools like Mem0, LangChain, or Redis.

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.