Add Skill: multi-agent-sequential-workflow

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

skills/code-review-agent/SKILL.md

@@ -1,76 +0,0 @@
----
-name: code-review-agent
-version: 1.0.0
-description: Automate code review process using a multi-step workflow with human-in-the-loop
-  approval.
-inputs:
-- Repository diff or code changeset (string)
-- User ID (string)
-steps:
-- 'Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`'
-- 'Step 2: Invoke the graph with initial parameters including the repository diff
-  and user ID, and set thread_id as a configurable parameter'
-- 'Step 3: The graph processes the input through various steps until completion or
-  human approval is needed'
-outputs:
-- Review result (dictionary containing messages, issues, etc.)
-tags: []
-metadata:
-  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# code-review-agent
-
-Automate code review process using a multi-step workflow with human-in-the-loop approval.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24,<2.0 langchain-mcp-adapters>=0.1 tenacity>=9.0
-```
-
-**Setup steps:**
-
-1. cp .env.example .env
-1. docker compose up -d
-1. pip install -e '.[dev]'
-
-## Key Files
-
-- `agentkit/workflow/code_review/graph.py - Defines the code review graph and its invocation method.`
-
-## Steps
-
-1. Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`
-2. Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter
-3. Step 3: The graph processes the input through various steps until completion or human approval is needed
-
-## Implementation Details
-
-```python
-graph = build_graph()
-thread_id = str(uuid.uuid4())
-result = graph.invoke(...)
-```
-
-## Inputs
-
-- Repository diff or code changeset (string)
-- User ID (string)
-
-## Outputs
-
-- Review result (dictionary containing messages, issues, etc.)
-
-## Failure Modes
-
-- Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention.
-
-## Source
-
-Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
-Confidence: 0.95

skills/code-review-agent/commands.md

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

skills/code-review-agent/examples.md

@@ -1,10 +0,0 @@
-# Examples: code-review-agent
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Repository diff or code changeset (string), User ID (string)
-# Process: Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter → Step 3: The graph processes the input through various steps until completion or human approval is needed
-# Outputs: Review result (dictionary containing messages, issues, etc.)
-```

skills/code-review-agent/metadata.json

@@ -1,24 +0,0 @@
-{
-  "name": "code-review-agent",
-  "version": "1.0.0",
-  "goal": "Automate code review process using a multi-step workflow with human-in-the-loop approval.",
-  "inputs": [
-    "Repository diff or code changeset (string)",
-    "User ID (string)"
-  ],
-  "steps": [
-    "Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`",
-    "Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter",
-    "Step 3: The graph processes the input through various steps until completion or human approval is needed"
-  ],
-  "outputs": [
-    "Review result (dictionary containing messages, issues, etc.)"
-  ],
-  "failure_modes": [
-    "Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention."
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is reusable for any code review process that requires a multi-step analysis with human-in-the-loop approval.",
-  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
-  "score": 1.0
-}

skills/code-review-agent/tests.md

@@ -1,9 +0,0 @@
-# Tests: code-review-agent
-
-## 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
 }