Add Skill: blacknode-graph-workflow

Extracted from: https://github.com/temiroff/Blacknode.git
Score: 1.0

config/settings.yaml

@@ -34,8 +34,8 @@ scout:
     - 'rag agent workflow'
     - 'tool calling workflow'
   filters:
-    stars_min: 15
-    pushed_after: 2026-05-01
+    stars_min: 10
+    pushed_after: 2026-02-01
     language: Python
     archived: false
     size_max_kb: 10000

pipeline/publisher.py

@@ -52,6 +52,18 @@ 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

@@ -137,6 +137,8 @@ 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/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-supervisor/SKILL.md

@@ -0,0 +1,84 @@
+---
+name: agent-supervisor
+version: 1.0.0
+description: Demonstrate a supervisor-worker architecture for intelligent task delegation
+  and real-time decision-making.
+inputs:
+- name: OPENAI_API_KEY
+  description: OpenAI API key for language models.
+- name: TAVILY_API_KEY
+  description: Tavily API key for search functionality.
+steps:
+- step: 1
+  action: Load environment variables.
+  details: Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.
+- step: 2
+  action: Configure LangChain tools.
+  details: Initialize TavilySearchResults and PythonREPLTool.
+- step: 3
+  action: Define agent nodes.
+  details: Create functions for the Researcher and Coder agents that process state
+    through their respective tasks.
+- step: 4
+  action: Set up supervisor agent.
+  details: Create a supervisor agent function that decides which worker should act
+    next based on user input.
+- step: 5
+  action: Build state graph.
+  details: Construct the state graph with nodes for each agent and edges connecting
+    them to the supervisor node.
+- step: 6
+  action: Add conditional edges.
+  details: Define conditions for transitioning between agents based on their responses.
+- step: 7
+  action: Compile graph.
+  details: Compile the state graph into a runnable workflow.
+- step: 8
+  action: Run example queries.
+  details: Stream through the workflow with example inputs to demonstrate its functionality.
+outputs:
+- name: 'Example 1: Code Hello World'
+  description: A demonstration of coding a simple hello world program.
+- name: 'Example 2: Research Report'
+  description: A demonstration of researching and writing a brief report on pikas.
+tags: []
+metadata:
+  source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
+  extracted_at: ''
+  confidence: 0.9
+---
+
+# agent-supervisor
+
+Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.
+
+## Steps
+
+1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
+2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
+3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
+4. {'step': 4, 'action': 'Set up supervisor agent.', 'details': 'Create a supervisor agent function that decides which worker should act next based on user input.'}
+5. {'step': 5, 'action': 'Build state graph.', 'details': 'Construct the state graph with nodes for each agent and edges connecting them to the supervisor node.'}
+6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
+7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
+8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}
+
+## Inputs
+
+- {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
+- {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
+
+## Outputs
+
+- {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}
+- {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
+
+## Failure Modes
+
+- {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
+- {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}
+
+## Source
+
+Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
+Confidence: 0.9

skills/agent-supervisor/commands.md

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

skills/agent-supervisor/examples.md

@@ -0,0 +1,10 @@
+# Examples: agent-supervisor
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}, {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
+# Process: {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'} → {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'} → {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
+# Outputs: {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}, {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
+```

skills/agent-supervisor/metadata.json

@@ -0,0 +1,81 @@
+{
+  "name": "agent-supervisor",
+  "version": "1.0.0",
+  "goal": "Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.",
+  "inputs": [
+    {
+      "name": "OPENAI_API_KEY",
+      "description": "OpenAI API key for language models."
+    },
+    {
+      "name": "TAVILY_API_KEY",
+      "description": "Tavily API key for search functionality."
+    }
+  ],
+  "steps": [
+    {
+      "step": 1,
+      "action": "Load environment variables.",
+      "details": "Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables."
+    },
+    {
+      "step": 2,
+      "action": "Configure LangChain tools.",
+      "details": "Initialize TavilySearchResults and PythonREPLTool."
+    },
+    {
+      "step": 3,
+      "action": "Define agent nodes.",
+      "details": "Create functions for the Researcher and Coder agents that process state through their respective tasks."
+    },
+    {
+      "step": 4,
+      "action": "Set up supervisor agent.",
+      "details": "Create a supervisor agent function that decides which worker should act next based on user input."
+    },
+    {
+      "step": 5,
+      "action": "Build state graph.",
+      "details": "Construct the state graph with nodes for each agent and edges connecting them to the supervisor node."
+    },
+    {
+      "step": 6,
+      "action": "Add conditional edges.",
+      "details": "Define conditions for transitioning between agents based on their responses."
+    },
+    {
+      "step": 7,
+      "action": "Compile graph.",
+      "details": "Compile the state graph into a runnable workflow."
+    },
+    {
+      "step": 8,
+      "action": "Run example queries.",
+      "details": "Stream through the workflow with example inputs to demonstrate its functionality."
+    }
+  ],
+  "outputs": [
+    {
+      "name": "Example 1: Code Hello World",
+      "description": "A demonstration of coding a simple hello world program."
+    },
+    {
+      "name": "Example 2: Research Report",
+      "description": "A demonstration of researching and writing a brief report on pikas."
+    }
+  ],
+  "failure_modes": [
+    {
+      "mode": "Invalid API keys",
+      "description": "The workflow may fail if the provided API keys are invalid or expired."
+    },
+    {
+      "mode": "Insufficient permissions",
+      "description": "The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality."
+    }
+  ],
+  "confidence": 0.9,
+  "explanation": "This workflow demonstrates a hierarchical multi-agent system where a supervisor agent makes routing decisions based on user input, delegating tasks to specialized worker agents (Researcher and Coder). It is designed to be reusable for similar task delegation scenarios.",
+  "source_repo": "https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git",
+  "score": 1.0
+}

skills/agent-supervisor/tests.md

@@ -1,4 +1,4 @@
-# Tests: agent-creation-and-management
+# Tests: agent-supervisor
 
 ## Test Checklist
 

skills/blacknode-graph-workflow/SKILL.md

@@ -0,0 +1,96 @@
+---
+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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,30 @@
+{
+  "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/blacknode-graph-workflow/tests.md

@@ -0,0 +1,9 @@
+# Tests: blacknode-graph-workflow
+
+## 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/langgraph-workflow-creation/SKILL.md

@@ -0,0 +1,83 @@
+---
+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

@@ -0,0 +1,6 @@
+# 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,26 @@
+{
+  "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

@@ -0,0 +1,9 @@
+# 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