Anthropic's Claude Skills introduce a lazy-loading approach to AI context management that could save developers hundreds of dollars monthly. The system uses reusable instruction sets that only load when relevant, contrasting sharply with MCP's eager metadata loading. This architectural shift addresses the fundamental inefficiency of traditional prompt engineering workflows.

Skills operate through progressive disclosure across three levels: metadata (name and description loaded at startup), body (full instructions loaded on invocation), and referenced files (additional context loaded on demand). A typical developer using multiple MCP servers faces roughly 32,000 tokens of idle metadata per message, costing approximately $0.16 in overhead for every conversation. This compounds to $160 monthly for active users.

The key distinction lies in loading patterns. MCP loads all tool metadata upfront regardless of usage, while Skills scale costs with actual invocation. Skills aren't replacing MCP but solving a different problem - they're reusable, auto-invoked instruction sets that minimize context window clutter. This approach fundamentally changes token economics by preventing the dumping of everything into context at once, improving latency, quality, and cost efficiency.