Skill UI

SOTA computer vision expert specializing in YOLO26, SAM 3, vision language models, and real-time spatial analysis. Provides guidance on designing, implementing, and optimizing advanced vision pipelines, including detection, segmentation, visual reasoning, depth estimation, and 3D reconstruction.

This skill covers strategies for compressing conversation history in AI agent sessions to manage context window limits. It focuses on optimizing tokens-per-task rather than just tokens-per-request. Techniques include anchored iterative summarization, opaque compression, and regenerative summaries. It also addresses artifact trail integrity and evaluation methods like probe-based testing to ensure critical information like file paths and decisions are preserved during compression.

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.

An intelligent context management specialist for capturing, serializing, and retrieving comprehensive project context across AI workflows. Supports semantic extraction, vector database integration, knowledge graphs, and multi-session collaboration.

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.

Context optimization extends effective capacity of limited context windows via compression, masking, caching, and partitioning. It aims to better utilize available capacity rather than increasing window size. Techniques include summarizing context, masking verbose outputs, reusing KV-cache, and partitioning work across sub-agents to reduce costs and latency in AI agent systems.

This skill covers strategies for optimizing Large Language Model context windows to prevent context rot. It includes techniques like summarization, trimming, routing, and prioritization to manage token budgets effectively. Developers can use these methods to build multi-turn conversation systems where context length is a constraint, leveraging tools like tiktoken and LangChain for implementation.

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.

Design collaborative AI agent teams with CrewAI: define agent roles, goals and backstories, build dependent tasks, and orchestrate crews via sequential or hierarchical processes with memory, tools, planning and flows.

A specialized quality gate skill designed to review complex, multi-source outputs generated by the CrossFrame framework. It rigorously checks reasoning fidelity, evidence boundaries, source anchors, and structural continuity. Use this when advanced verification of logical rigor and source integrity is required, ensuring outputs meet strict internal protocols and avoid common structural failures.

A comprehensive toolkit for building production-grade AI agents focused on crypto business development. This skill guides users through the entire token listing lifecycle: autonomously discovering promising assets across multiple chains, applying a weighted 100-point scoring model, verifying safety via advanced wallet forensics, and managing outreach pipelines with necessary human oversight and LLM cascade patterns.