ai-agent-builder

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 79d91524ef4c · 22 Oct 2026Report an issue or request a re-scan

• Publisherclaude-office-skills GitHub organizationOwner of the repository this skill is read from
• Source folderclaude-office-skills/skills/ai-agent-builderThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict79d91524ef4cA new audit runs when the source changes
• Repository statusNot archivedLast push 263 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns

---
name: ai-agent-builder
description: "Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns"
version: "1.0.0"
author: claude-office-skills
license: MIT

category: ai
tags:
  - ai-agent
  - chatgpt
  - openai
  - langchain
  - automation
department: Engineering

models:
  recommended:
    - claude-opus-4
    - claude-sonnet-4

capabilities:
  - agent_design
  - tool_integration
  - memory_management
  - multi_step_reasoning
  - conversation_flow

languages:
  - en
  - zh

related_skills:
  - deep-research
  - n8n-workflow
  - slack-workflows
---

# AI Agent Builder

Design and build AI agents with tools, memory, and multi-step reasoning capabilities. Covers ChatGPT, Claude, Gemini integration patterns based on n8n's 5,000+ AI workflow templates.

## Overview

This skill covers:
- AI agent architecture design
- Tool/function calling patterns
- Memory and context management
- Multi-step reasoning workflows
- Platform integrations (Slack, Telegram, Web)

---

## AI Agent Architecture

### Core Components

```
┌─────────────────────────────────────────────────────────────────┐
│                      AI AGENT ARCHITECTURE                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐       │
│  │   Input     │────▶│   Agent     │────▶│   Output    │       │
│  │  (Query)    │     │   (LLM)     │     │  (Response) │       │
│  └─────────────┘     └──────┬──────┘     └─────────────┘       │
│                             │                                   │
│         ┌───────────────────┼───────────────────┐              │
│         │                   │                   │              │
│         ▼                   ▼                   ▼              │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐       │
│  │   Tools     │     │   Memory    │     │  Knowledge  │       │
│  │ (Functions) │     │  (Context)  │     │   (RAG)     │       │
│  └─────────────┘     └─────────────┘     └─────────────┘       │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

### Agent Types

```yaml
agent_types:
  reactive_agent:
    description: "Single-turn response, no memory"
    use_case: simple_qa, classification
    complexity: low
    
  conversational_agent:
    description: "Multi-turn with conversation memory"
    use_case: chatbots, support
    complexity: medium
    
  tool_using_agent:
    description: "Can call external tools/APIs"
    use_case: data_lookup, actions
    complexity: medium
    
  reasoning_agent:
    description: "Multi-step planning and execution"
    use_case: complex_tasks, research
    complexity: high
    
  multi_agent:
    description: "Multiple specialized agents collaborating"
    use_case: complex_workflows
    complexity: very_high
```

---

## Tool Calling Pattern

### Tool Definition

```yaml
tool_definition:
  name: "get_weather"
  description: "Get current weather for a location"
  parameters:
    type: object
    properties:
      location:
        type: string
        description: "City name or coordinates"
      units:
        type: string
        enum: ["celsius", "fahrenheit"]
        default: "celsius"
    required: ["location"]
    
  implementation:
    type: api_call
    endpoint: "https://api.weather.com/v1/current"
    method: GET
    params:
      q: "{location}"
      units: "{units}"
```

### Common Tool Categories

```yaml
tool_categories:
  data_retrieval:
    - web_search: search the internet
    - database_query: query SQL/NoSQL
    - api_lookup: call external APIs
    - file_read: read documents
    
  actions:
    - send_email: send emails
    - create_calendar: schedule events
    - update_crm: modify CRM records
    - post_slack: send Slack messages
    
  computation:
    - calculator: math operations
    - code_interpreter: run Python
    - data_analysis: analyze datasets
    
  generation:
    - image_generation: create images
    - document_creation: generate docs
    - chart_creation: create visualizations
```

### n8n Tool Integration

```yaml
n8n_agent_workflow:
  nodes:
    - trigger:
        type: webhook
        path: "/ai-agent"
        
    - ai_agent:
        type: "@n8n/n8n-nodes-langchain.agent"
        model: openai_gpt4
        system_prompt: |
          You are a helpful assistant that can:
          1. Search the web for information
          2. Query our customer database
          3. Send emails on behalf of the user
          
        tools:
          - web_search
          - database_query
          - send_email
          
    - respond:
        type: respond_to_webhook
        data: "{{ $json.output }}"
```

---

## Memory Patterns

### Memory Types

```yaml
memory_types:
  buffer_memory:
    description: "Store last N messages"
    implementation: |
      messages = []
      def add_message(role, content):
          messages.append({"role": role, "content": content})
          if len(messages) > MAX_MESSAGES:
              messages.pop(0)
    use_case: simple_chatbots
    
  summary_memory:
    description: "Summarize conversation periodically"
    implementation: |
      When messages > threshold:
          summary = llm.summarize(messages[:-5])
          messages = [summary_message] + messages[-5:]
    use_case: long_conversations
    
  vector_memory:
    description: "Store in vector DB for semantic retrieval"
    implementation: |
      # Store
      embedding = embed(message)
      vector_db.insert(embedding, message)
      
      # Retrieve
      relevant = vector_db.search(query_embedding, k=5)
    use_case: knowledge_retrieval
    
  entity_memory:
    description: "Track entities mentioned in conversation"
    implementation: |
      entities = {}
      def update_entities(message):
          extracted = 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/claude-office-skills/skills
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Works in

1. 2026-10-22
   ✓SAFE B 89 / 100

   first audit · source 79d91524ef4c

Questions about ai-agent-builder

• github.com/claude-office-skills/skills/tree/main/ai-agent-builde — the audited source at commit 79d91524ef4c and every finding aboveObserved · 22 Oct 2026

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