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AI Engineering

Agent Delegation

by kangarooking

Agent Delegation is an AI Engineering skill for Claude Code, published by kangarooking in system-prompt-skills.

182 stars40 forkson kangarooking/system-prompt-skillsAdded 2026/09/06+3% in starsRepository updated 2026/05/04
agent-designagent-skillsai-agentscangjie-skillcontext-managementprompt-engineeringprompt-injectionsystem-prompttool-use
Install in seconds
Install Agent Delegation
Copy Agent Delegation into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/kangarooking/system-prompt-skills/tree/main/agent-delegation ~/.claude/skills/agent-delegation

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/kangarooking/system-prompt-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
agent-delegation/SKILL.md in kangarooking/system-prompt-skills
Installs to
~/.claude/skills/agent-delegation
Collection
One of 15 skills cataloged from this repository
Category
AI Engineering3278 skills

What Agent Delegation does

Agent Delegation describes how to split work across specialized sub-agents with isolated context, briefing-based handoffs, and validation gates. Use it when designing multi-agent systems, task lifecycles, or cross-tool coordination; not for single-agent routing.

Agent Delegation is cataloged under AI Engineering on DirSkills. Agent Delegation comes from a repository tagged agent-design, agent-skills, ai-agents, cangjie-skill and context-management.

Documentation

README

多代理与委派模式

R — 原文 (Reading)

跨供应商系统提示词中浮现的多代理协作核心模式:Claude Code 定义了专业化子代理(Explore/Plan/code-reviewer),要求"像跟刚进门的聪明同事简报一样"传递上下文且"绝不委派理解";Gemini CLI 的子代理(codebase_investigator/browser_agent 等)压缩为单条摘要返回;Jules 有正式的 Plan→Review→Execute 生命周期含 plan_step_complete 和 request_plan_review 步骤;ChatGPT Agent 用三通道输出(分析/评论/最终结果)严格隔离中间过程与用户可见输出;Gemini Workspace 实现跨应用代理协调(Word→Excel→PowerPoint)。

I — 方法论骨架 (Interpretation)

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Agent Delegation

  • What else does kangarooking publish alongside Agent Delegation?

    Agent Delegation is one of 15 skills that DirSkills catalogs from kangarooking/system-prompt-skills, the repository it ships in. Its siblings there include Citation System, Code Engineering and Context Management. Each one is a separate skill with its own page in this directory, installs the same way Agent Delegation does, and is maintained by kangarooking in that same repository. The rest of the collection is listed on the kangarooking/system-prompt-skills page.

  • How does Agent Delegation compare to other AI Engineering skills?

    Agent Delegation ranks #3043 by stars among the 3278 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Agent Delegation against them. Open each page to compare what they document and how they install.

More from kangarooking/system-prompt-skills

Agent Delegation is one of 15 skills cataloged on DirSkills from kangarooking/system-prompt-skills.

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Context Management

Context Management helps design token budgets, compression strategies, lazy loading, and persistent memory for long-running AI assistants and multi-session systems. Use it when conversation history, large files, or user preferences need to stay usable without overflowing the context window.
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Injection Defense

Injection Defense helps design system prompts that resist prompt injection, jailbreaks, social engineering, and trust-boundary breaks. Use it for agents or chatbots that process external content like documents, web pages, email, or memory.
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Memory System

Memory System designs how an AI stores, retrieves, applies, and updates user and project memories across sessions. Use it when building personalization, handling sensitive memories, or deciding when memory should be applied silently.
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