Skill UI

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.

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

Automates contributing guide creation in technical documentation; triggers on explicit requests, follows best practices, offers step-by-step help, generates code/config samples, and validates outputs for API docs, user guides, and architecture docs.

Orchestrates Control D operations via Composio’s Rube MCP, requiring tool discovery, active connections, and schema-compliant calls before executing workflows.

Guides using Rube MCP via Composio to discover Convertapi tool schemas, manage toolkit connections, and execute safe multi-step conversions without hardcoded slugs.

Guidelines for building Convex backends with TypeScript, detailing syntax, validators, schema design, queries, mutations, actions, scheduling, and file storage so you can implement Convex database operations, real-time updates, and cron jobs consistently.

Guides automating Convolo AI via Composio’s Rube MCP, covering tool discovery, connection validation, schema-compliant multi-tool execution, session reuse, and pagination handling for reliable workflows.

The official SDK for building applications that programmatically interact with GitHub Copilot. It provides comprehensive features including session management, custom tool definition (function calling), real-time streaming, and lifecycle hooks. Supports multiple languages, including Node.js, Python, Go, and .NET, making AI integration seamless across your tech stack.

Framework for improving existing marketing copy through seven sequential sweeps that check clarity, voice, benefits, evidence, specificity, and more while preserving the original message and brand tone.