Add Skill: agent-creation-and-management

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/agent-creation-and-management/SKILL.md

@@ -0,0 +1,73 @@
+---
+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

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

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

@@ -0,0 +1,27 @@
+{
+  "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

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

@@ -1,62 +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
 }