Add Skill: multi-agent-sequential-workflow

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

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/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. Install required dependencies using pip and ensure the BedrockModel is properly configured.
-1. Set up environment variables for API keys and model IDs
 
 ## 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/agent.py - Contains the sequential workflow logic for gathering and processing information.`
-- `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/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.
+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 guide content and prominently features the 'Suggested Web Pages' section.
+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 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 guide content and prominently features the 'Suggested Web Pages' section."
+    "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
 }