Agent 生成器
- 作者仓库星标 62,187
- 作者更新于 实时读取
- 作者仓库 learn-claude-code
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @shareAI-lab · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明（默认跨平台）
- 底层运行要求
- 无特殊要求
- 文件与系统权限
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- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。
需要注意： 未限定 allowed-tools，默认拥有全部工具权限。
---
name: agent-builder
description: | Build AI agents for any domain - customer service, research, operations, creative work, or spe…
category: AI 智能
runtime: 无特殊运行时
---
# agent-builder 输出预览
## PART A: 任务判断
- 适用问题：提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求：目标材料、限制条件、期望输出和验收方式。
- 证据边界：围绕“The Core Philosophy / The Three Elements / 1. Capabilities (What can it DO?)”读取原文规则，不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断：确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理，并标出输入、限制和预期结果。
- **02** 执行计划：优先按“The Core Philosophy / The Three Elements / 1. Capabilities (What can it DO?)”拆成步骤，说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果：给出可复制的命令、文件改动、检查清单或内容草稿，并说明如何继续迭代。
- **04** 风险边界：结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先小样例验证，再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。原文没有稳定的斜杠命令要求。安装验证后通常全局生效，直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是：读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“The Core Philosophy / The Three Elements / 1. Capabilities (What can it DO?)”工作；涉及文件或命令时，先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作；如果输出泛泛而谈，就补充输入、边界和验收标准后重跑。
---
name: agent-builder
description: | Build AI agents for any domain - customer service, research, operations, creative work, or spe…
category: AI 智能
source: shareAI-lab/learn-claude-code
---
# agent-builder
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理，核心价值是把输入、判断、执行、验证和交付边界固定下来，避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理，优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「The Core Philosophy / The Three Elements / 1. Capabilities (What can it DO?)」组织步骤，不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先跑小样例，确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown，不编造命令、平台或依赖。作者原文负责流程事实；仓库文件负责来源和命令；流狐只补充适用场景、限制和质量判断。
skill "agent-builder" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> The Core Philosophy / The Three Elements / 1. Capabilities (What can it DO?)
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
}Agent Builder
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
The Core Philosophy
The model already knows how to be an agent. Your job is to get out of the way.
An agent is not complex engineering. It's a simple loop that invites the model to act:
LOOP:
Model sees: context + available capabilities
Model decides: act or respond
If act: execute capability, add result, continue
If respond: return to userThat's it. The magic isn't in the code - it's in the model. Your code just provides the opportunity.
The Three Elements
1. Capabilities (What can it DO?)
Atomic actions the agent can perform: search, read, create, send, query, modify.
Design principle: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
2. Knowledge (What does it KNOW?)
Domain expertise injected on-demand: policies, workflows, best practices, schemas.
Design principle: Make knowledge available, not mandatory. Load it when relevant, not upfront.
3. Context (What has happened?)
The conversation history - the thread connecting actions into coherent behavior.
Design principle: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
Agent Design Thinking
Before building, understand:
- Purpose: What should this agent accomplish?
- Domain: What world does it operate in? (customer service, research, operations, creative...)
- Capabilities: What 3-5 actions are essential?
- Knowledge: What expertise does it need access to?
- Trust: What decisions can you delegate to the model?
CRITICAL: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.
Progressive Complexity
Start simple. Add complexity only when real usage reveals the need:
| Level | What to add | When to add it |
|---|---|---|
| Basic | 3-5 capabilities | Always start here |
| Planning | Progress tracking | Multi-step tasks lose coherence |
| Subagents | Isolated child agents | Exploration pollutes context |
| Skills | On-demand knowledge | Domain expertise needed |
Most agents never need to go beyond Level 2.
Domain Examples
Business: CRM queries, email, calendar, approvals Research: Database search, document analysis, citations Operations: Monitoring, tickets, notifications, escalation Creative: Asset generation, editing, collaboration, review
The pattern is universal. Only the capabilities change.
Key Principles
- The model IS the agent - Code just runs the loop
- Capabilities enable - What it CAN do
- Knowledge informs - What it KNOWS how to do
- Constraints focus - Limits create clarity
- Trust liberates - Let the model reason
- Iteration reveals - Start minimal, evolve from usage
Anti-Patterns
| Pattern | Problem | Solution |
|---|---|---|
| Over-engineering | Complexity before need | Start simple |
| Too many capabilities | Model confusion | 3-5 to start |
| Rigid workflows | Can't adapt | Let model decide |
| Front-loaded knowledge | Context bloat | Load on-demand |
| Micromanagement | Undercuts intelligence | Trust the model |
Resources
Philosophy & Theory:
references/agent-philosophy.md- Deep dive into why agents work
Implementation:
references/minimal-agent.py- Complete working agent (~80 lines)references/tool-templates.py- Capability definitionsreferences/subagent-pattern.py- Context isolation
Scaffolding:
scripts/init_agent.py- Generate new agent projects
The Agent Mindset
From: "How do I make the system do X?" To: "How do I enable the model to do X?"
From: "What's the workflow for this task?" To: "What capabilities would help accomplish this?"
The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.
Give the model capabilities and knowledge. Trust it to figure out the rest.
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核