Subagents

Skill-based encapsulation (splitting domain knowledge into sub-skills and sub-agents) is the mainstream path for AI tools to land in vertical scenarios.

Each subagent is essentially a Markdown file whose header contains the name, description, list of available tools and model selection, with the system prompt below.

The Subagents pattern isolates complex subtasks through independent contexts; a subagent's lengthy intermediate process does not affect the main agent's decision space, and the main agent receives only refined conclusions.

Anthropic's multi-agent research system shows that the subagent pattern is markedly effective in tasks requiring multi-step exploration, but token consumption increases significantly, so it should be avoided for simple tasks.

Production-grade agents typically combine five strategies: Prompt Caching, Compaction, Tool-result Clearing, Retrieval, and Subagents.

The recommended scope of claude-code-setup covers five categories: Hooks, Subagents, Skills, MCP Servers, and Plugins.

The context window refers to the maximum number of tokens a large model can process in a single inference, covering the sum of the input prompt and the output completion, and content exceeding the window will be truncated or cause the call to fail.

Claude Code can automate an entire workflow through three capabilities: Skills, Hooks, and Sub-agents.

Subagents are a multi-agent collaboration mechanism; Claude Code can decompose complex tasks and dispatch multiple sub-agents to complete them separately before aggregating the results.

A single Agent producing and validating its own output carries the risk of self-confirmation bias, as the model tends to approve the output it just generated; a Reviewer Agent with an independent context is more likely to find problems.