Add Skill: unifai-workflow-execution

Extracted from: https://github.com/redhat-community-ai-tools/UnifAI.git
Score: 1.0

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-creation-and-management/tests.md

@@ -1,9 +0,0 @@
-# Tests: agent-creation-and-management
-
-## 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

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

skills/unifai-workflow-execution/examples.md

@@ -4,7 +4,7 @@
 
 ```python
 # How to use this skill
-# Inputs: blueprint_id or blueprint_name, user_shortcut, user_question
+# 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 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)
+# 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: session_id, workflow_id
+# Outputs: {'name': 'execution_results', 'description': 'The output of the executed workflow, streamed as NDJSON over HTTP.'}
 ```

skills/unifai-workflow-execution/metadata.json

@@ -1,30 +1,53 @@
 {
   "name": "unifai-workflow-execution",
   "version": "1.0.0",
-  "goal": "Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.",
+  "goal": "Execute a multi-agent AI workflow defined in YAML or through the UI's drag-and-drop editor.",
   "inputs": [
-    "blueprint_id or blueprint_name",
+    {
-    "user_shortcut",
+      "name": "blueprint_path",
-    "user_question"
+      "description": "Path to the blueprint file (YAML) defining the multi-agent workflow."
+    },
+    {
+      "name": "execution_mode",
+      "description": "Execution mode: 'local' or 'distributed'."
+    }
   ],
   "steps": [
-    "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_name": "Load Blueprint",
-    "Step 3: Submit the session for background execution with the user prompt (submit_session method)",
+      "description": "Parse and validate the blueprint file to ensure it conforms to expected structure."
-    "Step 4: Poll session status until execution completes (poll_session_status method)"
+    },
+    {
+      "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."
+    }
   ],
   "outputs": [
-    "session_id",
+    {
-    "workflow_id"
+      "name": "execution_results",
+      "description": "The output of the executed workflow, streamed as NDJSON over HTTP."
+    }
   ],
   "failure_modes": [
-    "Blueprint name not found or not unique - error during blueprint resolution",
+    {
-    "Session creation fails - error from API response",
+      "mode_name": "Invalid Blueprint",
-    "Session submission fails - error from API response",
+      "description": "Blueprint file is not valid YAML or does not conform to expected structure."
-    "Polling session status fails - error from API response"
+    },
+    {
+      "mode_name": "Execution Engine Initialization Failure",
+      "description": "Failed to initialize the execution engine due to configuration issues or missing dependencies."
+    }
   ],
-  "confidence": 0.95,
+  "confidence": 0.9,
-  "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.",
+  "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.",
   "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
   "score": 1.0
 }