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-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

@@ -0,0 +1,9 @@
+# Tests: agent-supervisor
+
+## 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/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

skills/unifai-workflow-execution/SKILL.md

@@ -1,62 +1,96 @@
 ---
 name: unifai-workflow-execution
 version: 1.0.0
-description: Execute a multi-agent AI workflow defined in YAML or through the UI's
-  drag-and-drop editor.
+description: Execute a multi-agent workflow on the UnifAI platform using a specified
+  blueprint and user prompt.
 inputs:
-- name: blueprint_path
-  description: Path to the blueprint file (YAML) defining the multi-agent workflow.
-- name: execution_mode
-  description: 'Execution mode: ''local'' or ''distributed''.'
+- blueprint_id or blueprint_name
+- user_shortcut
+- user_question
 steps:
-- step_name: Load Blueprint
-  description: Parse and validate the blueprint file to ensure it conforms to expected
-    structure.
-- step_name: Initialize Execution Engine
-  description: Set up the execution engine based on the selected mode ('local' or
-    'distributed').
-- step_name: Execute Workflow
-  description: Run the multi-agent workflow, streaming node-by-node output as NDJSON
-    over HTTP.
-- step_name: Stream Results
-  description: Render and stream results in real time to clients subscribing to the
-    event stream.
+- 'Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id
+  method)'
+- 'Step 2: Create a new session from the blueprint (create_session method)'
+- 'Step 3: Submit the session for background execution with the user prompt (submit_session
+  method)'
+- 'Step 4: Poll session status until execution completes (poll_session_status method)'
 outputs:
-- name: execution_results
-  description: The output of the executed workflow, streamed as NDJSON over HTTP.
+- session_id
+- workflow_id
 tags: []
 metadata:
   source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
   extracted_at: ''
-  confidence: 0.9
+  confidence: 0.95
 ---
 
 # unifai-workflow-execution
 
-Execute a multi-agent AI workflow defined in YAML or through the UI's drag-and-drop editor.
+Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install requests urllib3
+```
+
+**Setup steps:**
+
+1. Install required dependencies using pip install requests urllib3
+1. Ensure the environment variables are set correctly (BLUEPRINT_ID, BLUEPRINT_NAME, USER_SHORTCUT, POLLING_INTERVAL, UNIFAI_BASE_URL)
+
+## Key Files
+
+- `scripts/execution_workflow.py - Main script for workflow execution`
 
 ## Steps
 
-1. {'step_name': 'Load Blueprint', 'description': 'Parse and validate the blueprint file to ensure it conforms to expected structure.'}
-2. {'step_name': 'Initialize Execution Engine', 'description': "Set up the execution engine based on the selected mode ('local' or 'distributed')."}
-3. {'step_name': 'Execute Workflow', 'description': 'Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP.'}
-4. {'step_name': 'Stream Results', 'description': 'Render and stream results in real time to clients subscribing to the event stream.'}
+1. Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)
+2. Step 2: Create a new session from the blueprint (create_session method)
+3. Step 3: Submit the session for background execution with the user prompt (submit_session method)
+4. Step 4: Poll session status until execution completes (poll_session_status method)
+
+## Implementation Details
+
+```python
+resolve_blueprint_id(client: UnifAIClient) -> str
+{...}
+# Resolve the blueprint ID from either direct ID or name lookup.
+```
+
+```python
+create_session(client: UnifAIClient, blueprint_id: str) -> str
+{...}
+# Create a new session from the blueprint.
+```
+
+```python
+submit_session(client: UnifAIClient, session_id: str) -> dict
+{...}
+# Submit the session for background execution with the user prompt.
+```
 
 ## Inputs
 
-- {'name': 'blueprint_path', 'description': 'Path to the blueprint file (YAML) defining the multi-agent workflow.'}
-- {'name': 'execution_mode', 'description': "Execution mode: 'local' or 'distributed'."}
+- blueprint_id or blueprint_name
+- user_shortcut
+- user_question
 
 ## Outputs
 
-- {'name': 'execution_results', 'description': 'The output of the executed workflow, streamed as NDJSON over HTTP.'}
+- session_id
+- workflow_id
 
 ## Failure Modes
 
-- {'mode_name': 'Invalid Blueprint', 'description': 'Blueprint file is not valid YAML or does not conform to expected structure.'}
-- {'mode_name': 'Execution Engine Initialization Failure', 'description': 'Failed to initialize the execution engine due to configuration issues or missing dependencies.'}
+- Blueprint name not found or not unique - error during blueprint resolution
+- Session creation fails - error from API response
+- Session submission fails - error from API response
+- Polling session status fails - error from API response
 
 ## Source
 
 Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
-Confidence: 0.9
+Confidence: 0.95

skills/unifai-workflow-execution/examples.md

@@ -4,7 +4,7 @@
 
 ```python
 # How to use this skill
-# Inputs: {'name': 'blueprint_path', 'description': 'Path to the blueprint file (YAML) defining the multi-agent workflow.'}, {'name': 'execution_mode', 'description': "Execution mode: 'local' or 'distributed'."}
-# Process: {'step_name': 'Load Blueprint', 'description': 'Parse and validate the blueprint file to ensure it conforms to expected structure.'} → {'step_name': 'Initialize Execution Engine', 'description': "Set up the execution engine based on the selected mode ('local' or 'distributed')."} → {'step_name': 'Execute Workflow', 'description': 'Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP.'}
-# Outputs: {'name': 'execution_results', 'description': 'The output of the executed workflow, streamed as NDJSON over HTTP.'}
+# Inputs: blueprint_id or blueprint_name, user_shortcut, user_question
+# Process: Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method) → Step 2: Create a new session from the blueprint (create_session method) → Step 3: Submit the session for background execution with the user prompt (submit_session method)
+# Outputs: session_id, workflow_id
 ```

skills/unifai-workflow-execution/metadata.json

@@ -1,53 +1,30 @@
 {
   "name": "unifai-workflow-execution",
   "version": "1.0.0",
-  "goal": "Execute a multi-agent AI workflow defined in YAML or through the UI's drag-and-drop editor.",
+  "goal": "Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.",
   "inputs": [
-    {
-      "name": "blueprint_path",
-      "description": "Path to the blueprint file (YAML) defining the multi-agent workflow."
-    },
-    {
-      "name": "execution_mode",
-      "description": "Execution mode: 'local' or 'distributed'."
-    }
+    "blueprint_id or blueprint_name",
+    "user_shortcut",
+    "user_question"
   ],
   "steps": [
-    {
-      "step_name": "Load Blueprint",
-      "description": "Parse and validate the blueprint file to ensure it conforms to expected structure."
-    },
-    {
-      "step_name": "Initialize Execution Engine",
-      "description": "Set up the execution engine based on the selected mode ('local' or 'distributed')."
-    },
-    {
-      "step_name": "Execute Workflow",
-      "description": "Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP."
-    },
-    {
-      "step_name": "Stream Results",
-      "description": "Render and stream results in real time to clients subscribing to the event stream."
-    }
+    "Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)",
+    "Step 2: Create a new session from the blueprint (create_session method)",
+    "Step 3: Submit the session for background execution with the user prompt (submit_session method)",
+    "Step 4: Poll session status until execution completes (poll_session_status method)"
   ],
   "outputs": [
-    {
-      "name": "execution_results",
-      "description": "The output of the executed workflow, streamed as NDJSON over HTTP."
-    }
+    "session_id",
+    "workflow_id"
   ],
   "failure_modes": [
-    {
-      "mode_name": "Invalid Blueprint",
-      "description": "Blueprint file is not valid YAML or does not conform to expected structure."
-    },
-    {
-      "mode_name": "Execution Engine Initialization Failure",
-      "description": "Failed to initialize the execution engine due to configuration issues or missing dependencies."
-    }
+    "Blueprint name not found or not unique - error during blueprint resolution",
+    "Session creation fails - error from API response",
+    "Session submission fails - error from API response",
+    "Polling session status fails - error from API response"
   ],
-  "confidence": 0.9,
-  "explanation": "This workflow is designed to execute multi-agent AI workflows defined in YAML blueprints or through the UI's drag-and-drop editor, providing real-time streaming of results.",
+  "confidence": 0.95,
+  "explanation": "This workflow is specific to the UnifAI platform and its multi-agent system, but can be adapted for similar systems with a similar architecture.",
   "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
   "score": 1.0
 }