Add Skill: agent-creation-and-management

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

pipeline/publisher.py

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

@@ -137,8 +137,6 @@ 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

@@ -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

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

skills/agent-supervisor/SKILL.md

@@ -1,84 +0,0 @@
----
-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

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

skills/agent-supervisor/examples.md

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

@@ -1,81 +0,0 @@
-{
-  "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
-}