# Agent Builder

Designs and builds AI agents, covering their architecture, tools, subagents and planning.

- Repository: https://github.com/shareAI-lab/learn-claude-code (79684 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-27
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -g -a claude-code -y`
Codex: `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -g -a codex -y`
Cursor: `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -g -a cursor -y`
Gemini CLI: `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -g -a gemini-cli -y`

## Description

Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

The rest: https://github.com/shareAI-lab/learn-claude-code/blob/HEAD/skills/agent-builder/SKILL.md