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1 Commits
| Author | SHA1 | Date | |
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| eb6be00602 |
@@ -52,18 +52,6 @@ def publish_skill(review_result, config):
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subprocess.run(["git", "config", "user.email", "hermes@agent.local"], cwd=repo_dir)
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subprocess.run(["git", "config", "user.name", "Hermes Pipeline"], cwd=repo_dir)
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# Check for duplicates in skills/ directory
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skills_dir = os.path.join(repo_dir, "skills")
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existing_skills = []
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if os.path.isdir(skills_dir):
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existing_skills = [d for d in os.listdir(skills_dir) if os.path.isdir(os.path.join(skills_dir, d))]
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if skill_name in existing_skills:
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return {
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"status": "SKIP",
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"reason": f"Skill '{skill_name}' already exists in skills/ directory",
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}
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# Create skill directory
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skill_dir = os.path.join(repo_dir, "skills", skill_name)
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os.makedirs(skill_dir, exist_ok=True)
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@@ -137,8 +137,6 @@ def main():
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if publish_output.get("status") == "PUBLISHED":
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print(f" ✓ Published! PR: {publish_output.get('pr_url', '')}")
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results["published"] += 1
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elif publish_output.get("status") == "SKIP":
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print(f" ⏸ Skipped: {publish_output.get('reason', '')}")
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else:
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print(f" ! {publish_output.get('status', '?')}: {publish_output.get('message', publish_output.get('error', ''))[:100]}")
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@@ -0,0 +1,73 @@
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---
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name: agent-creation-and-management
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version: 1.0.0
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description: Create and manage workplace AI agents using PipesHub's no-code agent
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builder.
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inputs:
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- Agent name with description
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- Action to perform (e.g., 'Gather facts about a company')
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steps:
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- 'Step 1: Open the PipesHub UI at http://localhost:3000'
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- 'Step 2: Navigate to the Agents section and click on ''Create Agent'''
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- 'Step 3: Enter the agent name and description in the form'
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- 'Step 4: Define the actions for the agent, such as ''Gather facts about a company'',
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using PipesHub''s no-code interface'
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- 'Step 5: Save and deploy the agent'
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outputs:
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- Agent created and deployed
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- Access URL for the new agent
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# agent-creation-and-management
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Create and manage workplace AI agents using PipesHub's no-code agent builder.
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## Setup
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**Setup steps:**
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1. Ensure Docker and Compose are installed
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1. Clone the repository: git clone https://github.com/pipeshub-ai/pipeshub-ai.git
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1. Run the installer: ./install.sh
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## Key Files
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- `N/A - Workflow implemented via UI`
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## Steps
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1. Step 1: Open the PipesHub UI at http://localhost:3000
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2. Step 2: Navigate to the Agents section and click on 'Create Agent'
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3. Step 3: Enter the agent name and description in the form
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4. Step 4: Define the actions for the agent, such as 'Gather facts about a company', using PipesHub's no-code interface
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5. Step 5: Save and deploy the agent
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## Implementation Details
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```python
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N/A - Workflow implemented via UI
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```
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## Inputs
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- Agent name with description
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- Action to perform (e.g., 'Gather facts about a company')
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## Outputs
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- Agent created and deployed
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- Access URL for the new agent
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## Failure Modes
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- If the agent creation fails due to missing required fields, ensure all necessary details are provided correctly
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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@@ -0,0 +1,6 @@
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# Commands: agent-creation-and-management
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## Available Commands
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- `/skill agent-creation-and-management` — Load this skill
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- `/run agent-creation-and-management` — Execute workflow
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@@ -0,0 +1,10 @@
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# Examples: agent-creation-and-management
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## Usage Example
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```python
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# How to use this skill
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# Inputs: Agent name with description, Action to perform (e.g., 'Gather facts about a company')
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# 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
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# Outputs: Agent created and deployed, Access URL for the new agent
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```
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@@ -0,0 +1,27 @@
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{
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"name": "agent-creation-and-management",
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"version": "1.0.0",
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"goal": "Create and manage workplace AI agents using PipesHub's no-code agent builder.",
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"inputs": [
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"Agent name with description",
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"Action to perform (e.g., 'Gather facts about a company')"
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],
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"steps": [
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"Step 1: Open the PipesHub UI at http://localhost:3000",
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"Step 2: Navigate to the Agents section and click on 'Create Agent'",
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"Step 3: Enter the agent name and description in the form",
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"Step 4: Define the actions for the agent, such as 'Gather facts about a company', using PipesHub's no-code interface",
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"Step 5: Save and deploy the agent"
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],
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"outputs": [
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"Agent created and deployed",
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"Access URL for the new agent"
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],
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"failure_modes": [
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"If the agent creation fails due to missing required fields, ensure all necessary details are provided correctly"
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific but can be adapted for different agents and actions within PipesHub's platform.",
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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"score": 1.0
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}
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+1
-1
@@ -1,4 +1,4 @@
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# Tests: langgraph-workflow-creation
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# Tests: agent-creation-and-management
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## Test Checklist
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@@ -1,83 +0,0 @@
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---
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name: langgraph-workflow-creation
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version: 1.0.0
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description: Create a LangGraph workflow to gather facts using SerperDevTool and process
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them with an AI agent.
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inputs:
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- API Key for SerperDevTool
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- Search Query
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steps:
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- 'Step 1: Import necessary modules from langgraph and langchain libraries'
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- 'Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function
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with SerperDevTool as the tool node'
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- 'Step 3: Define the search query and pass it to the agent for fact gathering'
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- 'Step 4: Process the gathered facts within the AI agent'
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outputs:
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- Processed Facts
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tags: []
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metadata:
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source_repo: https://github.com/jkmaina/LangGraphProjects.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-workflow-creation
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Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.
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## Setup
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**Dependencies:**
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```text
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pip install langchain serperdev
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```
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**Setup steps:**
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1. Install required libraries: pip install langchain serperdev
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1. Add API key to .env file: OPENAPI_API_KEY=your_api_key
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## Key Files
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- `agent.py - Contains the LangGraph agent creation logic`
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- `tool_node.py - Defines the SerperDevTool node`
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## Steps
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1. Step 1: Import necessary modules from langgraph and langchain libraries
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2. Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node
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3. Step 3: Define the search query and pass it to the agent for fact gathering
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4. Step 4: Process the gathered facts within the AI agent
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## Implementation Details
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```python
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import langgraph
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from serperdev import SerperDevTool
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def create_agent(api_key, query):
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tool = SerperDevTool(api_key)
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agent = langgraph.create_react_agent(tool=tool)
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facts = agent.run(query)
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return process_facts(facts)
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```
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## Inputs
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- API Key for SerperDevTool
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- Search Query
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## Outputs
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- Processed Facts
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## Failure Modes
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- API Key not provided
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- Invalid Search Query
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## Source
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Extracted from: [https://github.com/jkmaina/LangGraphProjects.git](https://github.com/jkmaina/LangGraphProjects.git)
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Confidence: 0.95
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@@ -1,6 +0,0 @@
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# Commands: langgraph-workflow-creation
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## Available Commands
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- `/skill langgraph-workflow-creation` — Load this skill
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- `/run langgraph-workflow-creation` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: langgraph-workflow-creation
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## Usage Example
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```python
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# How to use this skill
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# Inputs: API Key for SerperDevTool, Search Query
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# 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
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# Outputs: Processed Facts
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```
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@@ -1,26 +0,0 @@
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{
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"name": "langgraph-workflow-creation",
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"version": "1.0.0",
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"goal": "Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.",
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"inputs": [
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"API Key for SerperDevTool",
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"Search Query"
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],
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"steps": [
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"Step 1: Import necessary modules from langgraph and langchain libraries",
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"Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node",
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"Step 3: Define the search query and pass it to the agent for fact gathering",
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"Step 4: Process the gathered facts within the AI agent"
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],
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"outputs": [
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"Processed Facts"
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],
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"failure_modes": [
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"API Key not provided",
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"Invalid Search Query"
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific to fact gathering and can be adapted for different search queries or tools.",
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"source_repo": "https://github.com/jkmaina/LangGraphProjects.git",
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"score": 1.0
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}
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Block a user