Add Skill: agent-supervisor
Extracted from: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git Score: 1.0
This commit is contained in:
Hermes Pipeline
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---
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name: agent-supervisor
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version: 1.0.0
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description: Demonstrate a supervisor-worker architecture for intelligent task delegation
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and real-time decision-making.
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inputs:
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- name: OPENAI_API_KEY
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description: OpenAI API key for language models.
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- name: TAVILY_API_KEY
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description: Tavily API key for search functionality.
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steps:
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- step: 1
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action: Load environment variables.
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details: Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.
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- step: 2
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action: Configure LangChain tools.
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details: Initialize TavilySearchResults and PythonREPLTool.
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- step: 3
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action: Define agent nodes.
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details: Create functions for the Researcher and Coder agents that process state
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through their respective tasks.
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- step: 4
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action: Set up supervisor agent.
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details: Create a supervisor agent function that decides which worker should act
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next based on user input.
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- step: 5
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action: Build state graph.
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details: Construct the state graph with nodes for each agent and edges connecting
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them to the supervisor node.
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- step: 6
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action: Add conditional edges.
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details: Define conditions for transitioning between agents based on their responses.
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- step: 7
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action: Compile graph.
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details: Compile the state graph into a runnable workflow.
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- step: 8
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action: Run example queries.
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details: Stream through the workflow with example inputs to demonstrate its functionality.
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outputs:
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- name: 'Example 1: Code Hello World'
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description: A demonstration of coding a simple hello world program.
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- name: 'Example 2: Research Report'
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description: A demonstration of researching and writing a brief report on pikas.
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tags: []
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metadata:
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source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
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extracted_at: ''
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confidence: 0.9
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---
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# agent-supervisor
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Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.
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## Steps
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1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
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2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
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3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
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4. {'step': 4, 'action': 'Set up supervisor agent.', 'details': 'Create a supervisor agent function that decides which worker should act next based on user input.'}
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5. {'step': 5, 'action': 'Build state graph.', 'details': 'Construct the state graph with nodes for each agent and edges connecting them to the supervisor node.'}
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6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
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7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
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8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}
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## Inputs
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- {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
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- {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
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## Outputs
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- {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}
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- {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
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## Failure Modes
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- {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
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- {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}
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## Source
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Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
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Confidence: 0.9
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# Commands: agent-supervisor
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## Available Commands
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- `/skill agent-supervisor` — Load this skill
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- `/run agent-supervisor` — Execute workflow
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# Examples: agent-supervisor
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## Usage Example
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```python
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# How to use this skill
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# Inputs: {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}, {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
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# Process: {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'} → {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'} → {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
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# Outputs: {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}, {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
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```
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{
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"name": "agent-supervisor",
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"version": "1.0.0",
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"goal": "Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.",
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"inputs": [
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{
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"name": "OPENAI_API_KEY",
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"description": "OpenAI API key for language models."
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},
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{
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"name": "TAVILY_API_KEY",
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"description": "Tavily API key for search functionality."
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}
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],
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"steps": [
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{
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"step": 1,
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"action": "Load environment variables.",
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"details": "Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables."
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},
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{
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"step": 2,
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"action": "Configure LangChain tools.",
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"details": "Initialize TavilySearchResults and PythonREPLTool."
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},
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{
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"step": 3,
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"action": "Define agent nodes.",
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"details": "Create functions for the Researcher and Coder agents that process state through their respective tasks."
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},
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{
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"step": 4,
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"action": "Set up supervisor agent.",
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"details": "Create a supervisor agent function that decides which worker should act next based on user input."
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},
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{
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"step": 5,
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"action": "Build state graph.",
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"details": "Construct the state graph with nodes for each agent and edges connecting them to the supervisor node."
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},
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{
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"step": 6,
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"action": "Add conditional edges.",
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"details": "Define conditions for transitioning between agents based on their responses."
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},
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{
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"step": 7,
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"action": "Compile graph.",
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"details": "Compile the state graph into a runnable workflow."
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},
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{
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"step": 8,
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"action": "Run example queries.",
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"details": "Stream through the workflow with example inputs to demonstrate its functionality."
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}
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],
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"outputs": [
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{
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"name": "Example 1: Code Hello World",
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"description": "A demonstration of coding a simple hello world program."
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},
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{
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"name": "Example 2: Research Report",
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"description": "A demonstration of researching and writing a brief report on pikas."
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}
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],
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"failure_modes": [
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{
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"mode": "Invalid API keys",
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"description": "The workflow may fail if the provided API keys are invalid or expired."
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},
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{
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"mode": "Insufficient permissions",
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"description": "The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality."
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}
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],
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"confidence": 0.9,
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"explanation": "This workflow demonstrates a hierarchical multi-agent system where a supervisor agent makes routing decisions based on user input, delegating tasks to specialized worker agents (Researcher and Coder). It is designed to be reusable for similar task delegation scenarios.",
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"source_repo": "https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git",
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"score": 1.0
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}
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@@ -0,0 +1,9 @@
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# Tests: agent-supervisor
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## Test Checklist
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- [ ] Workflow has at least 3 steps
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- [ ] All inputs are defined
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- [ ] All outputs are defined
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- [ ] Failure modes are documented
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- [ ] Skill can be loaded without errors
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