Skill UI

This expert guides users through advanced, data-driven content marketing strategies. It covers AI-powered content creation, comprehensive SEO optimization, omnichannel distribution, social media management, email automation, and performance analytics. Use it when developing comprehensive content plans, maximizing online reach, or optimizing conversion funnels across various digital platforms.

Acts as a collaborative writing partner, helping you build outlines, research with citations, improve hooks, and review each section so blogs, tutorials, thought leadership, and docs get polished without losing your voice.

Automatically assists with generating Content Security Policies, offering step-by-step guidance, production-ready code, and validation against common standards whenever you mention or ask about CSP generation.

Helps plan content that drives traffic, builds authority, and generates leads by defining searchable or shareable topics, identifying pillars, and organizing editorial calendars tailored to business goals.

Automate Contentful headless CMS space operations from Claude Code, listing spaces, fetching metadata, and renaming spaces via the Composio Contentful toolkit without leaving your terminal.

Automates Contentful GraphQL workflows via the Composio Rube MCP toolkit by first querying RUBE_SEARCH_TOOLS for current schemas, verifying contentful_graphql connections, and executing schema-compliant tool calls with RUBE_MULTI_EXECUTE_TOOL while handling sessions, pagination, and memory.

Addresses the critical challenge of managing extremely long conversation histories (millions of tokens) in advanced AI agents, such as coding assistants. This technique moves beyond simple token minimization by implementing structured compression strategies (like Anchored Iterative Summarization) to prevent the loss of critical technical details—such as file paths, decisions, and error messages—ensuring the agent maintains functional context across complex, multi-step tasks. It emphasizes optimizing for 'tokens-per-task' rather than just tokens-per-request.

This skill analyzes the predictable ways language models lose performance as context length increases. It details core patterns, including 'lost-in-middle,' context poisoning, and distraction. Use this knowledge to diagnose unpredictable agent failures, design highly reliable systems for long document processing, and implement advanced context engineering strategies.

A comprehensive guide for treating project context as a managed, first-class artifact alongside code. It establishes systematic documentation (e.g., product vision, tech stack, workflow) to ensure consistent requirements, maintain team alignment, and optimize AI interactions throughout the entire development lifecycle. Essential for greenfield and brownfield projects.

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.

A sophisticated memory management system designed to recover, reconstruct, and maintain project context across complex, distributed, and multi-agent AI workflows. It specializes in advanced semantic memory rehydration, utilizing semantic vector search, relevance ranking, and conflict resolution to ensure seamless continuity and high fidelity of historical knowledge.

Use when working with context management context save