Skill UI

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.

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

This skill applies principles of behavioral psychology and choice architecture to redesign user experience (UX) flows. It is used when a product's conversion friction is rooted in interaction design, not just copy. By auditing cognitive load, defining optimal default paths, and inserting strategic commitment points, it guides users toward desired actions ethically, maximizing commitment while preserving autonomy. Ideal for improving onboarding, checkout funnels, and complex decision-making sequences.

This protocol simulates a diverse, anonymous jury of 3 to 5 specialized subagents to reach a committed, non-ambiguous verdict. It is designed to combat cognitive biases like anchoring and groupthink by running blind, independent opinions before structured deliberation. Ideal for high-stakes decisions across product development, architecture, strategy, and vendor choice where a single perspective is insufficient.

This tool acts as a meta-skill guide, helping users systematically determine the most appropriate cognitive framework when faced with complex problem-solving tasks. It maps the problem's domain and type (e.g., diagnosis, strategy, architecture) to select the optimal primary thinking skill, preventing guesswork or arbitrary stacking of tools. It ensures the chosen method is justified by mechanism fit and relevance.

This skill guides the creation of technical documentation (such as RFCs, READMEs, PR descriptions, and architecture guides) that minimizes the mental effort required for the reader. It emphasizes structural patterns like 'Lead with the answer,' 'Chunking,' and 'Progressive disclosure' to ensure content is scannable, actionable, and easy to review, significantly reducing cognitive load for maintainers and contributors.

This skill covers the architecture of intelligent agent memory, including short-term context windows and long-term vector stores. It explores cognitive frameworks like CoALA to organize semantic, episodic, and procedural memory. Focuses on retrieval strategies, chunking, and embeddings to ensure agents remember and recall information effectively across sessions.