The article examines the rise and fall of MCP, explains why simply adding AI‑driven Skills without rethinking workflows leads to “skill debt,” and outlines three hidden costs—expert knowledge capture, version drift, and context explosion—while proposing practical solutions such as experience capture, Skill‑as‑Code testing, and dynamic routing.
This article systematically explains the concepts of MCP, Agent, Skill, and Rule from an engineering viewpoint, highlighting their roles, differences from traditional API calls, and how they enable large language models to safely and autonomously interact with external tools.
This article walks through the design patterns, three‑level loading mechanism, and practical implementation steps for integrating reusable, domain‑specific Skills into an existing Agent system, covering both local and distributed deployments with Redis‑based versioning and sandboxed execution.
This article explains how AI Skills serve as structured knowledge bases that complement, rather than replace, Model Context Protocols, enhance Retrieval‑Augmented Generation, and drive three major trends—standardized agent stacks, low‑code knowledge engineering, and the emergence of personal AI agents.
This article explains the common difficulties of writing effective prompts, introduces the open‑source prompt‑optimizer skill that automatically selects and refines professional prompt frameworks, and provides step‑by‑step instructions for preparing materials, packaging the skill for Claude and Chrome, and installing it via GitHub repositories.
This article walks through common issues when using Vant's van-swipe for a horizontal card list—such as the last card being clipped and the peek effect being hidden—and presents a three‑layer nesting solution with detailed CSS, Vue code, a self‑test checklist, and a reusable skill definition to streamline future development.
The open‑source “planning‑with‑files” project reproduces Manus’s workflow, showing that AI’s long‑term stability hinges on external memory via Context Engineering, and provides a Claude Skill that structures tasks with markdown files to prevent forgetting and goal drift.
This article explains Claude's Skill system, how Skills differ from prompts, the concept of industry Workflows, the boundaries between Skills, Tools, and MCPs, and provides a step‑by‑step guide for designing Skills, wrapping legacy systems into Workflows, and building a hybrid Workflow‑plus‑Agent architecture for reliable, auditable automation.
This article explains how to replace cumbersome, city‑specific prompt strings with modular AI Skills, demonstrates the food‑diorama‑skill that generates 3D gourmet dioramas, and provides a step‑by‑step guide for connecting the DeepSeek V3.2 model to Claude Code using environment variables or the CC Switch GUI.
Anthropic’s recent addition of Skills to Claude introduces a modular, metadata‑driven approach that lets AI agents load specific capabilities on demand, offering a more efficient alternative to MCP’s context‑heavy tool integration and promising scalable, progressive disclosure for specialized workflows.
