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.
Fetches ai-agent-builder from claude-office-skills/skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/ai-agent-builder
Restart Cursor to activate ai-agent-builder. Access via /ai-agent-builder in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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.
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
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
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
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 usertools:- web_search
- database_query
- send_email
-respond:type: respond_to_webhook
data:"{{ $json.output }}"
Memory Patterns
Memory Types
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 = llm.extract_entities(message)
entities.update(extracted)use_case: personalized_assistants
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
