Add Skill: multi-agent-sequential-workflow

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

skills/agent-creation-and-management/SKILL.md

@@ -1,73 +0,0 @@
----
-name: agent-creation-and-management
-version: 1.0.0
-description: Create and manage workplace AI agents using PipesHub's no-code agent
-  builder.
-inputs:
-- Agent name with description
-- Action to perform (e.g., 'Gather facts about a company')
-steps:
-- 'Step 1: Open the PipesHub UI at http://localhost:3000'
-- 'Step 2: Navigate to the Agents section and click on ''Create Agent'''
-- 'Step 3: Enter the agent name and description in the form'
-- 'Step 4: Define the actions for the agent, such as ''Gather facts about a company'',
-  using PipesHub''s no-code interface'
-- 'Step 5: Save and deploy the agent'
-outputs:
-- Agent created and deployed
-- Access URL for the new agent
-tags: []
-metadata:
-  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# agent-creation-and-management
-
-Create and manage workplace AI agents using PipesHub's no-code agent builder.
-
-## Setup
-
-**Setup steps:**
-
-1. Ensure Docker and Compose are installed
-1. Clone the repository: git clone https://github.com/pipeshub-ai/pipeshub-ai.git
-1. Run the installer: ./install.sh
-
-## Key Files
-
-- `N/A - Workflow implemented via UI`
-
-## Steps
-
-1. Step 1: Open the PipesHub UI at http://localhost:3000
-2. Step 2: Navigate to the Agents section and click on 'Create Agent'
-3. Step 3: Enter the agent name and description in the form
-4. Step 4: Define the actions for the agent, such as 'Gather facts about a company', using PipesHub's no-code interface
-5. Step 5: Save and deploy the agent
-
-## Implementation Details
-
-```python
-N/A - Workflow implemented via UI
-```
-
-## Inputs
-
-- Agent name with description
-- Action to perform (e.g., 'Gather facts about a company')
-
-## Outputs
-
-- Agent created and deployed
-- Access URL for the new agent
-
-## Failure Modes
-
-- If the agent creation fails due to missing required fields, ensure all necessary details are provided correctly
-
-## Source
-
-Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
-Confidence: 0.95

skills/agent-creation-and-management/commands.md

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

skills/agent-creation-and-management/examples.md

@@ -1,10 +0,0 @@
-# Examples: agent-creation-and-management
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Agent name with description, Action to perform (e.g., 'Gather facts about a company')
-# Process: Step 1: Open the PipesHub UI at http://localhost:3000 → Step 2: Navigate to the Agents section and click on 'Create Agent' → Step 3: Enter the agent name and description in the form
-# Outputs: Agent created and deployed, Access URL for the new agent
-```

skills/agent-creation-and-management/metadata.json

@@ -1,27 +0,0 @@
-{
-  "name": "agent-creation-and-management",
-  "version": "1.0.0",
-  "goal": "Create and manage workplace AI agents using PipesHub's no-code agent builder.",
-  "inputs": [
-    "Agent name with description",
-    "Action to perform (e.g., 'Gather facts about a company')"
-  ],
-  "steps": [
-    "Step 1: Open the PipesHub UI at http://localhost:3000",
-    "Step 2: Navigate to the Agents section and click on 'Create Agent'",
-    "Step 3: Enter the agent name and description in the form",
-    "Step 4: Define the actions for the agent, such as 'Gather facts about a company', using PipesHub's no-code interface",
-    "Step 5: Save and deploy the agent"
-  ],
-  "outputs": [
-    "Agent created and deployed",
-    "Access URL for the new agent"
-  ],
-  "failure_modes": [
-    "If the agent creation fails due to missing required fields, ensure all necessary details are provided correctly"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is specific but can be adapted for different agents and actions within PipesHub's platform.",
-  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
-  "score": 1.0
-}

skills/agent-creation-and-management/tests.md

@@ -1,9 +0,0 @@
-# Tests: agent-creation-and-management
-
-## 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
 }