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Author SHA1 Message Date
Hermes Pipeline eb6be00602 Add Skill: agent-creation-and-management
Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0
2026-08-05 15:45:29 +00:00
11 changed files with 117 additions and 140 deletions
-12
View File
@@ -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)
-2
View File
@@ -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]}")
@@ -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
@@ -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
@@ -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
```
@@ -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
}
@@ -1,4 +1,4 @@
# Tests: langgraph-workflow-creation
# Tests: agent-creation-and-management
## Test Checklist
@@ -1,83 +0,0 @@
---
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
@@ -1,6 +0,0 @@
# Commands: langgraph-workflow-creation
## Available Commands
- `/skill langgraph-workflow-creation` — Load this skill
- `/run langgraph-workflow-creation` — Execute workflow
@@ -1,10 +0,0 @@
# 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
```
@@ -1,26 +0,0 @@
{
"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
}