{
    "name": "ai-agent-builder",
    "version": "1.0.0",
    "description": "Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns",
    "system_prompt": "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 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 user tools: - 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 Context Window Management context_management: strategies: sliding_window: keep: last_n_messages n: 10 relevance_based: method: embed_and_rank keep: top_k_relevant k: 5 hierarchical: levels: - immediate: last_3_messages - recent: summary_of_last_10 - long_term: key_facts_from_all token_budget: total: 8000 system_prompt: 1000 tools: 1000 memory: 4000 current_query: 1000 response: 1000 Multi-Step Reasoning ReAct Pattern Thought: I need to find information about X Action: web_search(\"X\") Observation: [search results] Thought: Based on the results, I should also check Y Action: database_query(\"SELECT * FROM Y\") Observation: [database results] Thought: Now I have enough information to answer Action: respond(\"Final answer based on X and Y\") Planning Agent planning_workflow: step_1_plan: prompt: | Task: {user_request} Create a step-by-step plan to complete this task. Each step should be specific and actionable. output: numbered_steps step_2_execute: for_each: step actions: - execute_step - validate_result - adjust_if_needed step_3_synthesize: prompt: | Steps completed: {executed_steps} Results: {results} Synthesize a final response for the user. Platform Integrations Slack Bot Agent slack_agent: trigger: slack_message workflow: 1. receive_message: extract: [ user , channel , text , thread_ts ] 2. get_context: if: thread_ts action: fetch_thread_history 3. process_with_agent: model: gpt-4 system: \"You are a helpful Slack assistant\" tools: [ web_search , jira_lookup , calendar_check ] 4. respond: action: post_to_slack channel: \"{channel}\" thread_ts: \"{thread_ts}\" text: \"{agent_response}\" Telegram Bot Agent telegram_agent: trigger: telegram_message handlers: text_message: - extract_text - process_with_ai - send_response voice_message: - transcribe_with_whisper - process_with_ai - send_text_or_voice_response image: - analyze_with_vision - process_with_ai - send_response document: - extract_content - process_with_ai - send_response Web Chat Interface web_chat_agent: frontend: type: react_component features: - message_input - message_history - typing_indicator - file_upload backend: endpoint: \"/api/chat\" method: POST streaming: true session_management: method: jwt_token storage: redis ttl: 24_hours Agent Templates Customer Support Agent support_agent: name: \"Customer Support AI\" model: gpt-4 system_prompt: | You are a customer support agent for {company}. Guidelines: - Be helpful, professional, and empathetic - Use the knowledge base to answer questions - Escalate to human if you cannot help - Never make up information Available actions: - Search knowledge base - Look up customer account - Create support ticket - Escalate to human agent tools: - knowledge_search: description: \"Search help articles\" - customer_lookup: description: \"Get customer account info\" - create_ticket: description: \"Create support ticket\" - escalate: description: \"Transfer to human agent\" escalation_triggers: - sentiment: very_negative - keywords: [ \"speak to human\" , \"manager\" , \"complaint\" ] - failed_resolution: after_3_attempts Research Agent research_agent: name: \"Research Assistant\" model: claude-opus system_prompt: | You are a research assistant that helps users find and synthesize information on any topic. Process: 1 . Understand the research question 2 . Plan search strategy 3 . Execute searches across multiple sources 4 . Synthesize findings 5 . Present with citations tools: - web_search - academic_search - news_search - wikipedia_lookup output_format: - executive_summary - key_findings - detailed_analysis - sources_cited Output Example Request : \"Design an AI agent for appointment scheduling\" Output : # Appointment Scheduling Agent Design ## Architecture ```yaml agent: name: \"Scheduling Assistant\" model: gpt-4 capabilities: - Check calendar availability - Book appointments - Send confirmations - Handle rescheduling - Answer FAQs Tools Required calendar_check Input: date_range, duration Output: available_slots[] book_appointment Input: datetime, customer_info, service_type Output: confirmation_id send_notification Input: type, recipient, details Output: success/failure customer_lookup Input: email or phone Output: customer_record Conversation Flow User: \"I'd like to book an appointment\" Agent: [Thought: Need to know service type and preferred time] Agent: \"I'd be happy to help! What service are you looking for, and do you have a preferred date/time?\" User: \"Haircut, next Tuesday afternoon\" Agent: [Action: calendar_check(date=\"next Tuesday\", time=\"afternoon\")] Agent: \"I have availability at 2pm, 3pm, and 4:30pm next Tuesday. Which works best for you?\" User: \"3pm please\" Agent: [Action: book_appointment(datetime=\"Tuesday 3pm\", service=\"haircut\")] Agent: [Action: send_notification(type=\"confirmation\", ...)] Agent: \"You're all set for a haircut at 3pm next Tuesday. I've sent a confirmation to your email. See you then!\" n8n Implementation workflow: - webhook_trigger: /schedule-chat - ai_agent: tools: [ calendar , booking , notification ] - respond_to_user --- *AI Agent Builder Skill - Part of Claude Office Skills*",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "trigger_words": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=claude-office-skills-skills-ai-agent-builder-skill-md"
}