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agent-creatorAgent 创建者

Agent Skill

agent-creator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

125

周安装

5

GitHub Stars

3

下载量

40
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agent-creator(Agent 创建者)
来源仓库:https://github.com/clubmediterranee/ai-core
仓库路径:skills/agent-creator
安装命令:
npx skills add https://github.com/clubmediterranee/ai-core --skill agent-creator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/clubmediterranee/ai-core --skill agent-creator

简介

agent-creator 用于创建和管理 Claude Code 中的子代理或多智能体协作架构。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中构建分层或协同式任务处理流程。
  • 区分父子代理通信模式和点对点会话机制,适配不同协作场景。
  • 需明确任务边界与生命周期,防止资源泄漏或死锁。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Agent Creator for Claude Code

Overview

Two distinct agent types in Claude Code:

TypeOfficial NameCommunicationUse When
ASubagentHierarchical (parent spawns child)Autonomous task, delegated by an orchestrator
BMulti-Agent SwarmPeer-to-peer via sessions (tmux)Coordinated agents that message each other

Step 0: Qualify the User's Intent

Before writing any agent, ask these questions:

  1. What task should the agent handle?
  2. Where will it live?

- Project: .claude/agents/ (shared with all project users) - Global: ~/.claude/agents/ (personal, all projects) - Plugin-bundled: skills/my-skill/agents/ (ships with a skill)

  1. Does it need to communicate with other running Claude Code sessions?

- No → Type A: Subagent - Yes → Type B: Multi-Agent Swarm

  1. Permissions: Should it run commands, edit files, or be read-only?
  2. Should it run in background or block the current session?

Frontmatter Standard

Every agent file must include project metadata (required by this project) and agent configuration fields.

Project Metadata (required on all agents)

created-at: YYYY-MM-DD
created-by: "Firstname Lastname <email@example.com>"
credits: https://...   # Optional — only if derived from external work

Always ask the user for their first name, last name, and email before writing the file. Never guess or skip created-by.

Agent Configuration Fields

FieldRequiredValuesNotes
nameYeslowercase-hyphens3–50 chars, start/end alphanumeric
descriptionYesText + <example> blocksPrimary triggering mechanism
modelNoinherit, sonnet, opus, haiku, full model IDDefault: inherit
colorNoblue cyan green yellow magenta redUI identifier
toolsNoArray of tool namesOmit = all tools
disallowedToolsNoArray of tool namesExplicitly deny
permissionModeNodefault acceptEdits dontAsk bypassPermissions planOverride permission prompts
maxTurnsNoIntegerCap agentic turns
backgroundNotrue/falseRun without blocking current session
effortNolow medium high maxReasoning effort level
isolationNoworktreeIsolated git worktree environment
memoryNouser project localPersistent memory scope
skillsNoArray of skill pathsPre-loaded skills at startup

Color guide: blue/cyan = analysis · green = generation · yellow = validation · red = security · magenta = creative/refactoring

Pre-loading Skills (skills field)

When creating an agent, suggest pre-loading relevant skills from the project. Skills give the agent additional domain expertise at startup.

Discover available skills dynamically — before suggesting anything, scan the project:

1. Glob: **/SKILL.md (search both skills/ and .claude/skills/, wherever they live)
2. For each result, read the `name` and `description` fields from the frontmatter
3. Based on the agent's domain, propose the relevant ones

Then ask the user: *"Should this agent have any skills pre-loaded?"* and show only the ones that match the agent's responsibilities.

Example frontmatter with skills:

skills:
  - skills/react-best-practices
  - skills/typescript-advanced-types

Type A: Subagent

A standalone agent spawned hierarchically. An orchestrator (Claude or another agent) delegates a task to it.

File Template

---
created-at: YYYY-MM-DD
created-by: "Firstname Lastname <email@example.com>"

name: my-agent
description: Use this agent when [conditions]. Examples:

<example>
Context: [Situation]
user: "[Request]"
assistant: "[Response using this agent]"
<commentary>
[Why this agent triggers here]
</commentary>
</example>

model: inherit
color: blue
tools: ["Read", "Grep", "Glob"]
---

You are [role] specializing in [domain].

**Your Core Responsibilities:**
1. [Primary responsibility]
2. [Secondary responsibility]

**Process:**
1. [Step 1]
2. [Step 2]

**Output Format:**
[What to produce and how to structure it]

Invocation

# Natural language — Claude decides
Use the my-agent subagent to analyze the codebase

# @-mention — forces this specific agent for one task
@"my-agent (agent)" check the auth module

Restricting Which Subagents an Orchestrator Can Spawn

In an orchestrator agent's frontmatter, limit spawnable subagents:

tools: Agent(worker, researcher), Read, Bash

Description Best Practices

The description field is the sole triggering mechanism. Include 2–4 <example> blocks covering:

  • Explicit request (user directly asks)
  • Proactive triggering (agent activates after relevant work)
  • Variations in phrasing

See references/triggering-examples.md for the full guide.

System Prompt Design

Write in second person (You are..., You will...). See references/system-prompt-design.md for complete patterns (Analysis, Generation, Validation, Orchestration) with structure templates and edge case guidance.


Type B: Multi-Agent Swarm

Multiple Claude Code sessions coordinating via shared state. Each session runs independently and notifies a coordinator when idle.

When to Use

  • Tasks that can be parallelized (multiple PRs, multiple services, multiple modules)
  • Workflows requiring specialized agents for different phases
  • Long-running work exceeding a single session's context
  • Independent tasks with explicit dependencies

Architecture

Coordinator session (e.g. "team-leader")
    ├── Worker session A ("auth-agent")    → works on Task 3.5
    ├── Worker session B ("db-agent")      → works on Task 4.2
    └── Worker session C ("api-agent")     → works on Task 5.1
         ↕ communicate via tmux send-keys

State File

Each worker session reads .claude/multi-agent-swarm.local.md to know its task and coordinator:

---
agent_name: auth-agent
task_number: 3.5
pr_number: TBD
coordinator_session: team-leader
enabled: true
dependencies: ["Task 3.4"]
additional_instructions: "Use JWT, not sessions"
---

# Task Assignment: Implement Authentication

## Requirements
- JWT token generation and validation
- Refresh token flow

## Success Criteria
- Auth endpoints pass all tests
- PR created and CI green

## Coordination
Depends on Task 3.4 (user model).
Report status to coordinator session 'team-leader'.

State File Fields

FieldRequiredDescription
agent_nameYesIdentifier for this agent in the swarm
task_numberYesTask ordering (e.g. 3.5)
coordinator_sessionYestmux session name of the coordinator
enabledYestrue/false — agent skips its hook if false
pr_numberNoAssociated PR number
dependenciesNoTask IDs that must complete first
additional_instructionsNoPer-agent override instructions

Idle Notification Hook

Add a Stop hook to each worker's .claude/settings.json that calls a notify script on idle. See examples/complete-agent-examples.md → Example 5 for the full settings.json block and notify-coordinator.sh script.

Coordinator System Prompt Pattern

You are the coordinator of a multi-agent swarm managing parallel development tasks.

**Your Core Responsibilities:**
1. Assign tasks to worker agents via their tmux sessions
2. Track task dependencies — only assign a task when its dependencies are complete
3. Handle worker notifications (agents message you when idle)
4. Consolidate completed work into a final report

**Coordination Process:**
1. Maintain a backlog of pending tasks with their dependencies
2. When a worker becomes idle: identify the next unblocked task and assign it
3. To assign a task: tmux send-keys -t <session> "<task description>" Enter
4. When all tasks complete: produce a summary of all PRs and outcomes

**State:** Track which tasks are pending/in-progress/done, and which session owns each.

Full Swarm Example

See examples/complete-agent-examples.md → "Example 5: Multi-Agent Swarm".



Quick Reference

Which type?

Does the agent need to message other running Claude Code sessions?
├── No  → Type A: Subagent
│         .claude/agents/my-agent.md
└── Yes → Type B: Multi-Agent Swarm
          .claude/multi-agent-swarm.local.md

Minimal Subagent

---
created-at: 2026-03-31
created-by: "Name <email>"
name: my-agent
description: Use this agent when... Examples: <example>...</example>
model: inherit
---
You are an agent that does X.
1. Step one
2. Step two
Output: [what to produce]

Reference Files

  • references/system-prompt-design.md — Patterns for Analysis, Generation, Validation, Orchestration agents
  • references/triggering-examples.md — Writing <example> blocks for reliable triggering
  • references/agent-creation-system-prompt.md — AI-assisted agent generation prompt

Example Files

  • examples/complete-agent-examples.md — Production-ready templates (subagents + swarm)
  • examples/agent-creation-prompt.md — AI-assisted generation workflow

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

39.28%
按下载量换算16

Claude

29.28%
按下载量换算12

Cursor

18.82%
按下载量换算8

Gemini CLI

10.15%
按下载量换算4

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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