Skill UI

This framework moves beyond simple storage solutions (like vector databases) to treat memory as a complex metabolic system. It provides tools and methodologies to audit, price, and govern an agent's memory, focusing especially on the forgetting policy. Key functions include cost profiling (construction vs. query energy), classifying stored records (fact/skill/log), and ensuring a mandatory forgetting policy to prevent state bloat and maintain performance.

This skill compiles user creative intent into executable image and video prompts for AI models. It specializes in character consistency, cinematic storyboards, and reference asset mapping for R2V/I2V/T2V tasks. It ensures visual contracts, narrative flow, and production-ready specifications without replacing model capabilities.

This skill enables systematic improvement of existing AI agents by analyzing performance metrics, identifying failure modes, and applying prompt engineering techniques. It involves establishing baselines, optimizing prompts through chain-of-thought and few-shot examples, refining role definitions, and validating changes through structured testing and A/B comparisons to ensure reliability and efficiency.

This skill guides the development of AI-powered products, focusing on LLM integration, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization. It ensures products are production-ready rather than failing demos.

A meta-skill that evaluates task complexity, selects and combines appropriate skills from local or catalog sources, records successful skill combinations via agent-memory-mcp, and guards against over-engineering simple tasks.

This skill covers predictable performance degradation patterns in language models as context length increases, including lost-in-middle effects, context poisoning, and distraction. It helps diagnose agent failures and design resilient systems by understanding attention mechanics and context engineering strategies for production environments.

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 strategies like progressive disclosure to optimize token usage and agent performance.

This skill specializes in advanced AI context engineering, managing dynamic information flows, vector databases, and knowledge graphs. It assists in building intelligent memory systems, optimizing context windows, and orchestrating multi-agent workflows. Ideal for enterprise-scale AI applications requiring coherent state management, RAG implementation, and semantic retrieval strategies to enhance system performance and reliability.

This skill detects when AI agents exploit human trust through social engineering, deceptive guidance, or false assurances. It identifies prompts that induce unsafe actions, request sensitive credentials, or impersonate authority figures. Use this when evaluating agent safety, security advice, or decision-influencing workflows. Skip for static information.

Expert prompt engineering skill that transforms basic instructions into production-ready LLM prompts using constitutional AI, chain-of-thought reasoning and model-specific optimization to boost accuracy and cut token costs.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. This skill covers few-shot learning, chain-of-thought prompting, prompt optimization, template systems, and system prompt design. It helps in designing complex prompts for production applications, optimizing consistency, and debugging inconsistent outputs. Ideal for developers building AI assistants and production-grade LLM applications.