Add Skill: multi-agent-sequential-workflow

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

skills/code-review-agent-workflow/SKILL.md

@@ -1,77 +0,0 @@
----
-name: code-review-agent-workflow
-version: 1.0.0
-description: Automate the code review process using a multi-step workflow with human-in-the-loop
-  approval.
-inputs:
-- Sample diff of code changes (str)
-- Repo context (dict)
-steps:
-- 'Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`'
-- 'Step 2: Invoke the graph with initial parameters including sample diff, repo context,
-  user ID, and other metadata'
-- 'Step 3: The graph processes the input through a series of steps, generating messages
-  and issues as it progresses'
-outputs:
-- Final result containing processed messages and issues (dict)
-tags: []
-metadata:
-  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# code-review-agent-workflow
-
-Automate the 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 fastapi>=0.115 uvicorn[standard]>=0.32 psycopg[binary]>=3.1 langgraph-checkpoint-postgres>=2.0 httpx>=0.27 python-dotenv>=1.0 redis>=5.0
-```
-
-**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 - Contains the `build_graph` function and graph invocation logic.`
-- `examples/run_code_review.py - Example script demonstrating how to run the code review agent.`
-
-## Steps
-
-1. Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`
-2. Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata
-3. Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
-
-## Implementation Details
-
-```python
-graph = build_graph()
-thread_id = str(uuid.uuid4())
-result = graph.invoke(...)
-```
-
-## Inputs
-
-- Sample diff of code changes (str)
-- Repo context (dict)
-
-## Outputs
-
-- Final result containing processed messages and issues (dict)
-
-## Failure Modes
-
-- Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed.
-
-## Source
-
-Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
-Confidence: 0.95

skills/code-review-agent-workflow/commands.md

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

skills/code-review-agent-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: code-review-agent-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Sample diff of code changes (str), Repo context (dict)
-# Process: Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata → Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
-# Outputs: Final result containing processed messages and issues (dict)
-```

skills/code-review-agent-workflow/metadata.json

@@ -1,24 +0,0 @@
-{
-  "name": "code-review-agent-workflow",
-  "version": "1.0.0",
-  "goal": "Automate the code review process using a multi-step workflow with human-in-the-loop approval.",
-  "inputs": [
-    "Sample diff of code changes (str)",
-    "Repo context (dict)"
-  ],
-  "steps": [
-    "Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`",
-    "Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata",
-    "Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses"
-  ],
-  "outputs": [
-    "Final result containing processed messages and issues (dict)"
-  ],
-  "failure_modes": [
-    "Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed."
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is reusable for any code review process that requires a multi-step analysis and human approval.",
-  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
-  "score": 1.0
-}

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

@@ -0,0 +1,84 @@
+---
+name: multi-agent-sequential-workflow
+version: 1.0.0
+description: Gather and process information from multiple agents to generate a comprehensive
+  travel guide.
+inputs:
+- User query with location
+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.'
+outputs:
+- Structured travel guide with key sections
+- Final client response
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# multi-agent-sequential-workflow
+
+Gather and process information from multiple agents to generate a comprehensive travel guide.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install python3 fastapi uvicorn strands bedrock-model
+```
+
+**Setup steps:**
+
+1. Install required dependencies using pip
+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/app.py - FastAPI app to handle user queries.`
+
+## 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.
+
+## Implementation Details
+
+```python
+research_output = researcher_agent(query, stream=False)
+```
+
+```python
+guide_output = travel_guide_agent(planner_prompt, stream=False)
+```
+
+```python
+final_response = writer_agent(writer_prompt, stream=False)
+```
+
+## Inputs
+
+- User query with location
+
+## 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
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95

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

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

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

@@ -0,0 +1,10 @@
+# Examples: multi-agent-sequential-workflow
+
+## Usage Example
+
+```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.
+# Outputs: Structured travel guide with key sections, Final client response
+```

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

@@ -0,0 +1,24 @@
+{
+  "name": "multi-agent-sequential-workflow",
+  "version": "1.0.0",
+  "goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
+  "inputs": [
+    "User query with location"
+  ],
+  "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."
+  ],
+  "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"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.",
+  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
+  "score": 1.0
+}

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

@@ -1,4 +1,4 @@
-# Tests: code-review-agent-workflow
+# Tests: multi-agent-sequential-workflow
 
 ## Test Checklist