Skill UI

A sophisticated hybrid memory system designed specifically for AI agents. It provides a persistent, searchable knowledge bank to store, retrieve, and manage long-term knowledge, including architectural designs, operational patterns, and critical decision points. By syncing with project documentation, it ensures that crucial institutional knowledge remains accessible throughout the development lifecycle, enhancing agent reliability and consistency.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Persistent LLM conversation memory that manages short-term, long-term, and entity-focused layers to store, retrieve, and consolidate memorable context for smarter conversational AI agents.

This skill is designed for advanced error detective work. It analyzes complex log streams, correlates stack traces across distributed systems, and detects anomalous error patterns. It helps identify root causes by correlating errors with system deployments and changes, providing actionable findings for immediate fixes and long-term prevention strategies.

This skill addresses the challenge of handling temporary CDN links for videos generated by Kling AI. It provides a comprehensive, multi-cloud solution to download the video asset and persistently upload it to major cloud storage platforms, including AWS S3, Google Cloud Storage (GCS), and Azure Blob Storage. It facilitates building robust, long-term media pipelines and ensures metadata preservation.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

A comprehensive skill to identify, analyze, prioritize, and track technical debt across large codebases. It provides three interconnected tools: a Debt Scanner, a Debt Prioritizer (using cost-of-delay frameworks), and a Debt Dashboard. This system helps engineering teams make data-driven decisions to balance new features with necessary maintenance and refactoring work, ensuring the long-term sustainability of software systems.

Guide teams through a structured PESTEL assessment of external political, economic, social, technological, environmental, and legal forces to surface risks and opportunities for product strategy, market entry, and long-term planning.

A structured decision-making framework for high-stakes, painful business choices—such as layoffs or pivots—where there are no perfect options. It guides you through evaluating reversibility, long-term impact, and stakeholder communication to find the least wrong path.

A comprehensive tool for analyzing long-term health data, tracking trends across multiple dimensions such as weight, vital signs, symptoms, medication adherence, lab results, and mood. It identifies significant changes, correlations between lifestyle factors and health metrics, and provides data-driven insights, risk assessments, and predictive recommendations for proactive health management.

This advanced agent deeply simulates the cognitive structure, strategic mindset, and analytical frameworks of Bill Gates. It moves beyond simple imitation, allowing users to analyze complex problems through the lens of systemic thinking, platform economics, and long-term strategic planning—reflecting his career from founding Microsoft to global philanthropy. Ideal for business strategy, technology forecasting, and high-level problem decomposition.

This advanced agent simulates the deep investment philosophy of Warren Buffett, blending Benjamin Graham's quantitative rigor with Charlie Munger's qualitative insight. It helps users perform disciplined analyses by applying frameworks like 'Margin of Safety' and identifying durable business 'moats,' ensuring recommendations are free from emotional speculation. Ideal for deep dives into value investing and long-term capital allocation.