Skill UI

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.

Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP, including right-sizing, pricing models, tagging, and cost monitoring.

This skill guides you in implementing Command Query Responsibility Segregation (CQRS) patterns for scalable software architectures. It focuses on separating read and write models to optimize query performance and support event-sourced systems. Use this when you need to scale reads independently from writes or handle complex query scenarios. It is suitable for building high-performance reporting systems where detailed read/write data models are required.

A powerful plugin enabling Claude Code to delegate complex tasks to external AI CLIs (such as OpenAI Codex or Google Antigravity/Gemini) without exiting the conversation. It supports multi-model workflows, allowing users to seek a second opinion, cross-check deep analysis, or delegate side tasks (like research or code review). The core value is that Claude synthesizes the raw outputs, providing a critical review and reconciliation of findings across all consulted models.

Analyzes existing codebases to produce comprehensive long-form technical manuals and ebooks, covering architecture, design rationale, data models, integration points, deployment and security for onboarding and reviews.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

Guide to selecting and optimizing embedding models for vector search and RAG applications, covering model comparison, chunking, fine-tuning, and dimension reduction.

Agent-native knowledge compiler for embodied-AI development. Consults, searches, and verifies a typed graph of papers, ROS packages, models, datasets, and benchmarks to assemble grounded, source-linked implementation workflows and capabilities.

Synchronize retail prices between ERP/POS masters and Electronic Shelf Labels (SES-imagotag, ZKONG, Pricer, Hanshow, SOLUM) using SHA256 delta watermarks, monotonic idempotency keys, RF gateway throttling, and e-paper battery models to guarantee shelf-edge price parity.

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

This skill rewrites user prompts to reduce false-positive safety triggers in AI models without bypassing policies or altering intent. It specifically targets cyber, bio/chem, and reasoning classifiers by abstracting sensitive domains and reframing offensive language into defensive equivalents. Useful for developers and researchers needing to test or discuss sensitive topics within safety guardrails.