Add Skill: langgraph-multi-agent-router

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

skills/blacknode-graph-workflow/SKILL.md

@@ -1,96 +0,0 @@
----
-name: blacknode-graph-workflow
-version: 1.0.0
-description: Build and execute node-based AI workflows with LLM agents and processing
-  nodes
-inputs:
-- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
-- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
-  etc.)
-- Data sources (URLs, text content, or other inputs for the workflow)
-steps:
-- Initialize a blacknode.Graph instance to create the workflow structure
-- Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
-  FileWrite)
-- Define edges connecting nodes to establish data flow between them
-- Execute the graph using cook() to run the workflow and generate outputs
-outputs:
-- Processed results from the final node (e.g., printed text, written files, or generated
-  data)
-- Graph execution status and any errors encountered during execution
-tags: []
-metadata:
-  source_repo: https://github.com/temiroff/Blacknode.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# blacknode-graph-workflow
-
-Build and execute node-based AI workflows with LLM agents and processing nodes
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
-```
-
-**Setup steps:**
-
-1. Install blacknode package: pip install blacknode
-1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
-1. Create a Graph instance and add nodes with inputs/outputs
-1. Define node connections in g._edges list
-1. Execute with g.cook() to run the workflow and capture results
-
-## Key Files
-
-- `blacknode/blacknode.py (Graph class implementation)`
-- `examples/hello_agent.py (simple LLM agent workflow)`
-- `examples/converted_nvidia_nim.py (NIM model workflow)`
-
-## Steps
-
-1. Initialize a blacknode.Graph instance to create the workflow structure
-2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
-3. Define edges connecting nodes to establish data flow between them
-4. Execute the graph using cook() to run the workflow and generate outputs
-
-## Implementation Details
-
-```python
-g = bn.Graph()
-```
-
-```python
-g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
-```
-
-```python
-result = g.cook(output_node, 'value')
-```
-
-## Inputs
-
-- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
-- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
-- Data sources (URLs, text content, or other inputs for the workflow)
-
-## Outputs
-
-- Processed results from the final node (e.g., printed text, written files, or generated data)
-- Graph execution status and any errors encountered during execution
-
-## Failure Modes
-
-- Missing or invalid model API key causing graph initialization failure
-- Incorrect node connections or missing edge definitions leading to runtime errors
-- Model not found or unavailable in the specified environment causing execution failure
-- Graph edges not properly defined or mismatched causing cook() to fail
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/blacknode-graph-workflow/commands.md

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

skills/blacknode-graph-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: blacknode-graph-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic), Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.), Data sources (URLs, text content, or other inputs for the workflow)
-# Process: Initialize a blacknode.Graph instance to create the workflow structure → Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite) → Define edges connecting nodes to establish data flow between them
-# Outputs: Processed results from the final node (e.g., printed text, written files, or generated data), Graph execution status and any errors encountered during execution
-```

skills/blacknode-graph-workflow/metadata.json

@@ -1,30 +0,0 @@
-{
-  "name": "blacknode-graph-workflow",
-  "version": "1.0.0",
-  "goal": "Build and execute node-based AI workflows with LLM agents and processing nodes",
-  "inputs": [
-    "Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)",
-    "Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)",
-    "Data sources (URLs, text content, or other inputs for the workflow)"
-  ],
-  "steps": [
-    "Initialize a blacknode.Graph instance to create the workflow structure",
-    "Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)",
-    "Define edges connecting nodes to establish data flow between them",
-    "Execute the graph using cook() to run the workflow and generate outputs"
-  ],
-  "outputs": [
-    "Processed results from the final node (e.g., printed text, written files, or generated data)",
-    "Graph execution status and any errors encountered during execution"
-  ],
-  "failure_modes": [
-    "Missing or invalid model API key causing graph initialization failure",
-    "Incorrect node connections or missing edge definitions leading to runtime errors",
-    "Model not found or unavailable in the specified environment causing execution failure",
-    "Graph edges not properly defined or mismatched causing cook() to fail"
-  ],
-  "confidence": 0.95,
-  "explanation": "Blacknode provides a standardized Graph-based workflow pattern where users create node graphs using the blacknode.Graph class. This pattern is reusable across projects as it follows a consistent structure: initialize a graph, add nodes with defined inputs/outputs, connect them with edges, and execute with cook(). The examples demonstrate this pattern with LLM agents and text processing pipelines, making it adaptable to various robotics and AI workflows.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}

skills/langgraph-multi-agent-router/SKILL.md

@@ -0,0 +1,96 @@
+---
+name: langgraph-multi-agent-router
+version: 1.0.0
+description: Orchestrate a multi-agent workflow where specialized agents collaborate
+  sequentially to gather information, structure it, and generate a final response
+inputs:
+- User query string (e.g., destination location)
+- BedrockModel configuration (model_id, temperature, top_p)
+- Pre-configured agents with specific system prompts and tool sets
+steps:
+- Researcher agent executes with system prompt to gather raw destination facts (places,
+  history, accommodations, food, web pages) using BedrockModel and available tools
+  (calculator, current_time)
+- Travel guide agent receives raw research output and structures it into labeled sections
+  (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
+  Suggested Web Pages)
+- Writer agent receives the structured guide and synthesizes it into a professional
+  client-facing response with clear formatting and emphasis on the suggested web pages
+outputs:
+- Raw research data (JSON string containing gathered facts)
+- Structured guide content (markdown-formatted travel guide with labeled sections)
+- Final client response (professional formatted response ready for delivery)
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-multi-agent-router
+
+Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph bedrock-model pydantic
+```
+
+**Setup steps:**
+
+1. Install langchain and langgraph packages
+1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
+1. Create three Agent instances with specific system prompts and tool sets
+1. Initialize LangGraph with the agent chain and run the workflow
+
+## Key Files
+
+- `agents/langchain_langgraph/00-basic-agent/agent.py`
+- `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
+- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
+
+## Steps
+
+1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
+2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
+3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
+
+## Implementation Details
+
+```python
+Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
+```
+
+```python
+Travel guide agent receives raw output and formats into 5 labeled sections
+```
+
+```python
+Writer agent takes structured guide and writes professional client response
+```
+
+## Inputs
+
+- User query string (e.g., destination location)
+- BedrockModel configuration (model_id, temperature, top_p)
+- Pre-configured agents with specific system prompts and tool sets
+
+## Outputs
+
+- Raw research data (JSON string containing gathered facts)
+- Structured guide content (markdown-formatted travel guide with labeled sections)
+- Final client response (professional formatted response ready for delivery)
+
+## Failure Modes
+
+- Researcher agent fails to gather sufficient data or returns incomplete results
+- Travel guide agent fails to structure information correctly or produces unreadable output
+- Writer agent fails to format the final response properly or loses key information from the guide
+
+## 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/langgraph-multi-agent-router/commands.md

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

skills/langgraph-multi-agent-router/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-multi-agent-router
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: User query string (e.g., destination location), BedrockModel configuration (model_id, temperature, top_p), Pre-configured agents with specific system prompts and tool sets
+# Process: Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) → Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) → Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
+# Outputs: Raw research data (JSON string containing gathered facts), Structured guide content (markdown-formatted travel guide with labeled sections), Final client response (professional formatted response ready for delivery)
+```

skills/langgraph-multi-agent-router/metadata.json

@@ -0,0 +1,29 @@
+{
+  "name": "langgraph-multi-agent-router",
+  "version": "1.0.0",
+  "goal": "Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response",
+  "inputs": [
+    "User query string (e.g., destination location)",
+    "BedrockModel configuration (model_id, temperature, top_p)",
+    "Pre-configured agents with specific system prompts and tool sets"
+  ],
+  "steps": [
+    "Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)",
+    "Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)",
+    "Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages"
+  ],
+  "outputs": [
+    "Raw research data (JSON string containing gathered facts)",
+    "Structured guide content (markdown-formatted travel guide with labeled sections)",
+    "Final client response (professional formatted response ready for delivery)"
+  ],
+  "failure_modes": [
+    "Researcher agent fails to gather sufficient data or returns incomplete results",
+    "Travel guide agent fails to structure information correctly or produces unreadable output",
+    "Writer agent fails to format the final response properly or loses key information from the guide"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a reusable multi-stage agent pattern where specialized agents collaborate in sequence. The Researcher agent gathers raw information using a domain-specific model, the Travel Guide agent structures that information into a consistent format, and the Writer agent synthesizes the final output. This pattern can be adapted to other domains (e.g., code generation, data analysis, research workflows) by swapping the agent types and system prompts while maintaining the same three-step structure.",
+  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
+  "score": 1.0
+}

skills/langgraph-multi-agent-router/tests.md

@@ -1,4 +1,4 @@
-# Tests: blacknode-graph-workflow
+# Tests: langgraph-multi-agent-router
 
 ## Test Checklist