Agent 生成器

AI 智能社区

作者仓库星标 62,187
作者仓库 learn-claude-code

解读按原文结构重写，命令、链接、术语均保留；右侧可核对作者原始 SKILL.md

方法与流程

Agent Design Thinking：Before building, understand: Purpose: What should this agent accomplish? Domain: What world does it operate in? (customer service, research, operations, creative...)

适用与边界

当前原文没有单列适用场景。
Anti-Patterns：Pattern · Problem · Solution Over-engineering · Complexity before need · Start simple Too many capabilities · Model confusion · 3-5 to start

原文中的明确线索

要点：「The model already knows how to be an agent. Your job is to get out of the way.」、「That's it.」、「Design principle」、「Purpose」、「Domain」、「Capabilities」、「Knowledge」、「Trust」
文件与命令：references/agent-philosophy.md、references/minimal-agent.py、references/tool-templates.py、references/subagent-pattern.py、scripts/initagent.py

流狐整理：以上内容来自当前 SKILL.md 的章节与原词；未补写作者没有声明的工具、兼容性或能力。

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 user

That'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 definitions
references/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.

流狐档案作者与许可取自来源；运行、权限和网络为流狐检测或估算

流狐分类: AI 智能
作者声明 Agent: 未找到明确声明；不据此推断已兼容或已测试
静态检查: 88 / 100 · 启发式扫描，不代表运行安全
作者 / 版本 / 许可: @shareAI-lab · 未声明 license
流狐 Token 估算: 低消耗
流狐接入估算: 需简单配置
是否需要外部 API Key: 未发现要求
检测到的系统要求: 未声明
底层运行要求: 未声明
检测到的文件与系统行为: 只读
允许写入 / 修改
Shell 执行
检测到的网络行为: 仅限本地
安装命令数: 无（仅作为资料）

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。

输出预览 agent-builder.preview

# 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.

SKILL.md SKILL.md

# 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 user
```

**That'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?

…

证据边界与执行链路

作者原文负责流程事实；流狐只索引当前章节、要点、文件与命令。

章节    -> The Core Philosophy → The Three Elements → 1. Capabilities (What can it DO?) → 2. Knowledge (What does it KNOW?) → 3. Context (What has happened?) → Agent Design Thinking
要点    -> The model already knows how to be an agent. Your job is to get out of the way. · That's it. · Design principle · Purpose · Domain · Capabilities · Knowledge · Trust
文件/命令 -> references/agent-philosophy.md · references/minimal-agent.py · references/tool-templates.py · references/subagent-pattern.py · scripts/initagent.py
内容 SHA-256 -> 0e2a69f1e92a