Add Skill: multi-agent-sequential-workflow

Extracted from: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
Score: 1.0

config/settings.yaml

@@ -34,8 +34,8 @@ scout:
     - 'rag agent workflow'
     - 'tool calling workflow'
   filters:
-    stars_min: 10
-    pushed_after: 2026-02-01
+    stars_min: 15
+    pushed_after: 2026-05-01
     language: Python
     archived: false
     size_max_kb: 10000

pipeline/publisher.py

@@ -52,18 +52,6 @@ def publish_skill(review_result, config):
         subprocess.run(["git", "config", "user.email", "hermes@agent.local"], cwd=repo_dir)
         subprocess.run(["git", "config", "user.name", "Hermes Pipeline"], cwd=repo_dir)
 
-        # Check for duplicates in skills/ directory
-        skills_dir = os.path.join(repo_dir, "skills")
-        existing_skills = []
-        if os.path.isdir(skills_dir):
-            existing_skills = [d for d in os.listdir(skills_dir) if os.path.isdir(os.path.join(skills_dir, d))]
-
-        if skill_name in existing_skills:
-            return {
-                "status": "SKIP",
-                "reason": f"Skill '{skill_name}' already exists in skills/ directory",
-            }
-
         # Create skill directory
         skill_dir = os.path.join(repo_dir, "skills", skill_name)
         os.makedirs(skill_dir, exist_ok=True)

run.py

@@ -137,8 +137,6 @@ def main():
         if publish_output.get("status") == "PUBLISHED":
             print(f"    ✓ Published! PR: {publish_output.get('pr_url', '')}")
             results["published"] += 1
-        elif publish_output.get("status") == "SKIP":
-            print(f"    ⏸ Skipped: {publish_output.get('reason', '')}")
         else:
             print(f"    ! {publish_output.get('status', '?')}: {publish_output.get('message', publish_output.get('error', ''))[:100]}")
 

skills/agent-supervisor/SKILL.md

@@ -1,84 +0,0 @@
----
-name: agent-supervisor
-version: 1.0.0
-description: Demonstrate a supervisor-worker architecture for intelligent task delegation
-  and real-time decision-making.
-inputs:
-- name: OPENAI_API_KEY
-  description: OpenAI API key for language models.
-- name: TAVILY_API_KEY
-  description: Tavily API key for search functionality.
-steps:
-- 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.
-- step: 4
-  action: Set up supervisor agent.
-  details: Create a supervisor agent function that decides which worker should act
-    next based on user input.
-- step: 5
-  action: Build state graph.
-  details: Construct the state graph with nodes for each agent and edges connecting
-    them to the supervisor node.
-- step: 6
-  action: Add conditional edges.
-  details: Define conditions for transitioning between agents based on their responses.
-- step: 7
-  action: Compile graph.
-  details: Compile the state graph into a runnable workflow.
-- step: 8
-  action: Run example queries.
-  details: Stream through the workflow with example inputs to demonstrate its functionality.
-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.
-tags: []
-metadata:
-  source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
-  extracted_at: ''
-  confidence: 0.9
----
-
-# agent-supervisor
-
-Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.
-
-## Steps
-
-1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
-2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
-3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
-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.'}
-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.'}
-6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
-7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
-8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}
-
-## Inputs
-
-- {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
-- {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
-
-## 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.'}
-
-## Failure Modes
-
-- {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
-- {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}
-
-## Source
-
-Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
-Confidence: 0.9

skills/agent-supervisor/commands.md

@@ -1,6 +0,0 @@
-# Commands: agent-supervisor
-
-## Available Commands
-
-- `/skill agent-supervisor` — Load this skill
-- `/run agent-supervisor` — Execute workflow

skills/agent-supervisor/examples.md

@@ -1,10 +0,0 @@
-# Examples: agent-supervisor
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}, {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
-# 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.'}
-# 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.'}
-```

skills/agent-supervisor/metadata.json

@@ -1,81 +0,0 @@
-{
-  "name": "agent-supervisor",
-  "version": "1.0.0",
-  "goal": "Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.",
-  "inputs": [
-    {
-      "name": "OPENAI_API_KEY",
-      "description": "OpenAI API key for language models."
-    },
-    {
-      "name": "TAVILY_API_KEY",
-      "description": "Tavily API key for search functionality."
-    }
-  ],
-  "steps": [
-    {
-      "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."
-    },
-    {
-      "step": 4,
-      "action": "Set up supervisor agent.",
-      "details": "Create a supervisor agent function that decides which worker should act next based on user input."
-    },
-    {
-      "step": 5,
-      "action": "Build state graph.",
-      "details": "Construct the state graph with nodes for each agent and edges connecting them to the supervisor node."
-    },
-    {
-      "step": 6,
-      "action": "Add conditional edges.",
-      "details": "Define conditions for transitioning between agents based on their responses."
-    },
-    {
-      "step": 7,
-      "action": "Compile graph.",
-      "details": "Compile the state graph into a runnable workflow."
-    },
-    {
-      "step": 8,
-      "action": "Run example queries.",
-      "details": "Stream through the workflow with example inputs to demonstrate its functionality."
-    }
-  ],
-  "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."
-    }
-  ],
-  "failure_modes": [
-    {
-      "mode": "Invalid API keys",
-      "description": "The workflow may fail if the provided API keys are invalid or expired."
-    },
-    {
-      "mode": "Insufficient permissions",
-      "description": "The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality."
-    }
-  ],
-  "confidence": 0.9,
-  "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.",
-  "source_repo": "https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git",
-  "score": 1.0
-}

skills/agent-supervisor/tests.md

@@ -1,9 +0,0 @@
-# Tests: agent-supervisor
-
-## Test Checklist
-
-- [ ] Workflow has at least 3 steps
-- [ ] All inputs are defined
-- [ ] All outputs are defined
-- [ ] Failure modes are documented
-- [ ] Skill can be loaded without errors

skills/langgraph-agent-workflow/SKILL.md

@@ -1,115 +0,0 @@
----
-name: langgraph-agent-workflow
-version: 1.0.0
-description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
-  RAG, code execution, and citation generation
-inputs:
-- LangGraph chain configuration files defining agent workflows
-- SerperDevTool integration for LLM tool access
-- React agent creation scripts via create_react_agent
-- Knowledge graph retrieval and citation generation pipelines
-steps:
-- 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
-  a LangGraph chain that combines retrieval, reasoning, and response generation using
-  SerperDevTool for tool access'
-- 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
-  agent that can interact with the LangGraph chain'
-- 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
-  graph retrieval (Neo4j/ArangoDB) with citation generation'
-- 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
-  for code-related tasks'
-- "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
-  \ \u2192 agent response in a single LangGraph workflow"
-outputs:
-- Reusable LangGraph chain definition (pyfile) with configurable steps
-- React agent frontend component that can be deployed independently
-- RAG pipeline that generates block citations and grounded answers
-- Code execution sandbox for artifact generation
-- Documentation for parameterizing workflows for different tasks
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-agent-workflow
-
-Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
-```
-
-**Setup steps:**
-
-1. Install LangGraph and SerperDevTool dependencies
-1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
-1. Define chain topology with retrieval, reasoning, and response steps
-1. Build React agent frontend using create_react_agent
-1. Test multi-step agent workflows end-to-end
-
-## Key Files
-
-- `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
-- `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
-- `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
-- `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
-
-## Steps
-
-1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
-2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
-3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
-4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
-5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
-
-## Implementation Details
-
-```python
-chain = LangGraph()
-```
-
-```python
-chain.add_step(SerperDevToolAgent())
-```
-
-```python
-agent = create_react_agent(chain, SerperDevToolAgent())
-```
-
-```python
-workflow = chain.start()
-```
-
-## Inputs
-
-- LangGraph chain configuration files defining agent workflows
-- SerperDevTool integration for LLM tool access
-- React agent creation scripts via create_react_agent
-- Knowledge graph retrieval and citation generation pipelines
-
-## Outputs
-
-- Reusable LangGraph chain definition (pyfile) with configurable steps
-- React agent frontend component that can be deployed independently
-- RAG pipeline that generates block citations and grounded answers
-- Code execution sandbox for artifact generation
-- Documentation for parameterizing workflows for different tasks
-
-## Failure Modes
-
-- GraphDB connection failures if Neo4j/ArangoDB is not properly configured
-- Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
-- LLM tool access errors if SerperDevTool is not properly initialized
-- Agent timeout if complex multi-step reasoning exceeds time limits
-- Sandbox execution failures if code has security vulnerabilities or infinite loops
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/langgraph-agent-workflow/commands.md

@@ -1,6 +0,0 @@
-# Commands: langgraph-agent-workflow
-
-## Available Commands
-
-- `/skill langgraph-agent-workflow` — Load this skill
-- `/run langgraph-agent-workflow` — Execute workflow

skills/langgraph-agent-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-agent-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: LangGraph chain configuration files defining agent workflows, SerperDevTool integration for LLM tool access, React agent creation scripts via create_react_agent, Knowledge graph retrieval and citation generation pipelines
-# Process: Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access → Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain → Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
-# Outputs: Reusable LangGraph chain definition (pyfile) with configurable steps, React agent frontend component that can be deployed independently, RAG pipeline that generates block citations and grounded answers, Code execution sandbox for artifact generation, Documentation for parameterizing workflows for different tasks
-```

skills/langgraph-agent-workflow/metadata.json

@@ -1,36 +0,0 @@
-{
-  "name": "langgraph-agent-workflow",
-  "version": "1.0.0",
-  "goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
-  "inputs": [
-    "LangGraph chain configuration files defining agent workflows",
-    "SerperDevTool integration for LLM tool access",
-    "React agent creation scripts via create_react_agent",
-    "Knowledge graph retrieval and citation generation pipelines"
-  ],
-  "steps": [
-    "Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access",
-    "Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
-    "Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
-    "Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
-    "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
-  ],
-  "outputs": [
-    "Reusable LangGraph chain definition (pyfile) with configurable steps",
-    "React agent frontend component that can be deployed independently",
-    "RAG pipeline that generates block citations and grounded answers",
-    "Code execution sandbox for artifact generation",
-    "Documentation for parameterizing workflows for different tasks"
-  ],
-  "failure_modes": [
-    "GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
-    "Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
-    "LLM tool access errors if SerperDevTool is not properly initialized",
-    "Agent timeout if complex multi-step reasoning exceeds time limits",
-    "Sandbox execution failures if code has security vulnerabilities or infinite loops"
-  ],
-  "confidence": 0.95,
-  "explanation": "PipesHub provides a reusable LangGraph-based agent workflow framework that can be parameterized for different tasks. The core pattern involves defining a LangGraph chain with SerperDevTool integration for tool access, creating a React agent wrapper, and configuring RAG pipelines with citation generation. This framework can be reused across RAG, code execution, and research workflows by adjusting the chain definition and agent configuration.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
-  "score": 1.0
-}

skills/langgraph-agent-workflow/tests.md

@@ -1,9 +0,0 @@
-# Tests: langgraph-agent-workflow
-
-## Test Checklist
-
-- [ ] Workflow has at least 3 steps
-- [ ] All inputs are defined
-- [ ] All outputs are defined
-- [ ] Failure modes are documented
-- [ ] Skill can be loaded without errors

skills/langgraph-workflow-creation/SKILL.md

@@ -1,83 +0,0 @@
----
-name: langgraph-workflow-creation
-version: 1.0.0
-description: Create a LangGraph workflow to gather facts using SerperDevTool and process
-  them with an AI agent.
-inputs:
-- API Key for SerperDevTool
-- Search Query
-steps:
-- 'Step 1: Import necessary modules from langgraph and langchain libraries'
-- 'Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function
-  with SerperDevTool as the tool node'
-- 'Step 3: Define the search query and pass it to the agent for fact gathering'
-- 'Step 4: Process the gathered facts within the AI agent'
-outputs:
-- Processed Facts
-tags: []
-metadata:
-  source_repo: https://github.com/jkmaina/LangGraphProjects.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# langgraph-workflow-creation
-
-Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain serperdev
-```
-
-**Setup steps:**
-
-1. Install required libraries: pip install langchain serperdev
-1. Add API key to .env file: OPENAPI_API_KEY=your_api_key
-
-## Key Files
-
-- `agent.py - Contains the LangGraph agent creation logic`
-- `tool_node.py - Defines the SerperDevTool node`
-
-## Steps
-
-1. Step 1: Import necessary modules from langgraph and langchain libraries
-2. Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node
-3. Step 3: Define the search query and pass it to the agent for fact gathering
-4. Step 4: Process the gathered facts within the AI agent
-
-## Implementation Details
-
-```python
-import langgraph
-from serperdev import SerperDevTool
-
-def create_agent(api_key, query):
-    tool = SerperDevTool(api_key)
-    agent = langgraph.create_react_agent(tool=tool)
-    facts = agent.run(query)
-    return process_facts(facts)
-```
-
-## Inputs
-
-- API Key for SerperDevTool
-- Search Query
-
-## Outputs
-
-- Processed Facts
-
-## Failure Modes
-
-- API Key not provided
-- Invalid Search Query
-
-## Source
-
-Extracted from: [https://github.com/jkmaina/LangGraphProjects.git](https://github.com/jkmaina/LangGraphProjects.git)
-Confidence: 0.95

skills/langgraph-workflow-creation/commands.md

@@ -1,6 +0,0 @@
-# Commands: langgraph-workflow-creation
-
-## Available Commands
-
-- `/skill langgraph-workflow-creation` — Load this skill
-- `/run langgraph-workflow-creation` — Execute workflow

skills/langgraph-workflow-creation/examples.md

@@ -1,10 +0,0 @@
-# Examples: langgraph-workflow-creation
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: API Key for SerperDevTool, Search Query
-# Process: Step 1: Import necessary modules from langgraph and langchain libraries → Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node → Step 3: Define the search query and pass it to the agent for fact gathering
-# Outputs: Processed Facts
-```

skills/langgraph-workflow-creation/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "langgraph-workflow-creation",
-  "version": "1.0.0",
-  "goal": "Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.",
-  "inputs": [
-    "API Key for SerperDevTool",
-    "Search Query"
-  ],
-  "steps": [
-    "Step 1: Import necessary modules from langgraph and langchain libraries",
-    "Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node",
-    "Step 3: Define the search query and pass it to the agent for fact gathering",
-    "Step 4: Process the gathered facts within the AI agent"
-  ],
-  "outputs": [
-    "Processed Facts"
-  ],
-  "failure_modes": [
-    "API Key not provided",
-    "Invalid Search Query"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is specific to fact gathering and can be adapted for different search queries or tools.",
-  "source_repo": "https://github.com/jkmaina/LangGraphProjects.git",
-  "score": 1.0
-}

skills/langgraph-workflow-creation/tests.md

@@ -1,9 +0,0 @@
-# Tests: langgraph-workflow-creation
-
-## Test Checklist
-
-- [ ] Workflow has at least 3 steps
-- [ ] All inputs are defined
-- [ ] All outputs are defined
-- [ ] Failure modes are documented
-- [ ] Skill can be loaded without errors

skills/multi-agent-sequential-workflow/SKILL.md

@@ -9,10 +9,10 @@ steps:
 - 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
   to gather raw facts about the destination.'
 - 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
-  a structured travel guide based on the user''s request and raw information provided
+  a structured travel guide based on the user''s request and the raw information provided
   by the researcher.'
 - 'Step 3: Writer agent (agent.py) formats the final response, including the structured
-  guide content and prominently features the ''Suggested Web Pages'' section.'
+  travel guide content and prominently featuring the ''Suggested Web Pages'' section.'
 outputs:
 - Structured travel guide with key sections
 - Final client response
@@ -32,24 +32,23 @@ Gather and process information from multiple agents to generate a comprehensive
 **Dependencies:**
 
 ```text
-pip install python3 fastapi uvicorn strands bedrock-model
+pip install python langchain strands
 ```
 
 **Setup steps:**
 
-1. Install required dependencies using pip
-1. Set up environment variables for API keys and model IDs
+1. Install required dependencies using pip and ensure the BedrockModel is properly configured.
 
 ## Key Files
 
-- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
-- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the sequential workflow logic for gathering and processing information.`
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - Provides a FastAPI endpoint to interact with the multi-agent system.`
 
 ## Steps
 
 1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
-2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.
-3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
+2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and the raw information provided by the researcher.
+3. Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section.
 
 ## Implementation Details
 
@@ -76,7 +75,7 @@ final_response = writer_agent(writer_prompt, stream=False)
 
 ## Failure Modes
 
-- Network issues during API calls could lead to incomplete data collection or processing failures
+- Specific failure scenario with mitigation: If any of the agents fail to process their tasks (e.g., network issues, model errors), the workflow will fail. Mitigation involves robust error handling and fallback mechanisms.
 
 ## Source
 

skills/multi-agent-sequential-workflow/examples.md

@@ -5,6 +5,6 @@
 ```python
 # How to use this skill
 # Inputs: User query with location
-# Process: Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination. → Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
+# Process: Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination. → Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and the raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section.
 # Outputs: Structured travel guide with key sections, Final client response
 ```

skills/multi-agent-sequential-workflow/metadata.json

@@ -7,18 +7,18 @@
   ],
   "steps": [
     "Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
-    "Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.",
-    "Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section."
+    "Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and the raw information provided by the researcher.",
+    "Step 3: Writer agent (agent.py) formats the final response, including the structured travel guide content and prominently featuring the 'Suggested Web Pages' section."
   ],
   "outputs": [
     "Structured travel guide with key sections",
     "Final client response"
   ],
   "failure_modes": [
-    "Network issues during API calls could lead to incomplete data collection or processing failures"
+    "Specific failure scenario with mitigation: If any of the agents fail to process their tasks (e.g., network issues, model errors), the workflow will fail. Mitigation involves robust error handling and fallback mechanisms."
   ],
   "confidence": 0.95,
-  "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.",
+  "explanation": "This workflow is specific to generating travel guides but can be adapted for other types of structured content creation.",
   "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
   "score": 1.0
 }