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Author SHA1 Message Date
Hermes Pipeline 730115b418 Add Skill: code-review-agent
Extracted from: https://github.com/itszhaoziyan-n/AgentKit.git
Score: 1.0
2026-08-05 15:46:16 +00:00
11 changed files with 117 additions and 196 deletions
-12
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@@ -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
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@@ -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]}")
-84
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@@ -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
-6
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@@ -1,6 +0,0 @@
# Commands: agent-supervisor
## Available Commands
- `/skill agent-supervisor` — Load this skill
- `/run agent-supervisor` — Execute workflow
-10
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@@ -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.'}
```
-81
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@@ -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
}
+76
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@@ -0,0 +1,76 @@
---
name: code-review-agent
version: 1.0.0
description: Automate code review process using a multi-step workflow with human-in-the-loop
approval.
inputs:
- Repository diff or code changeset (string)
- User ID (string)
steps:
- 'Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`'
- 'Step 2: Invoke the graph with initial parameters including the repository diff
and user ID, and set thread_id as a configurable parameter'
- 'Step 3: The graph processes the input through various steps until completion or
human approval is needed'
outputs:
- Review result (dictionary containing messages, issues, etc.)
tags: []
metadata:
source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
extracted_at: ''
confidence: 0.95
---
# code-review-agent
Automate code review process using a multi-step workflow with human-in-the-loop approval.
## Setup
**Dependencies:**
```text
pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24,<2.0 langchain-mcp-adapters>=0.1 tenacity>=9.0
```
**Setup steps:**
1. cp .env.example .env
1. docker compose up -d
1. pip install -e '.[dev]'
## Key Files
- `agentkit/workflow/code_review/graph.py - Defines the code review graph and its invocation method.`
## Steps
1. Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`
2. Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter
3. Step 3: The graph processes the input through various steps until completion or human approval is needed
## Implementation Details
```python
graph = build_graph()
thread_id = str(uuid.uuid4())
result = graph.invoke(...)
```
## Inputs
- Repository diff or code changeset (string)
- User ID (string)
## Outputs
- Review result (dictionary containing messages, issues, etc.)
## Failure Modes
- Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention.
## Source
Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
Confidence: 0.95
+6
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@@ -0,0 +1,6 @@
# Commands: code-review-agent
## Available Commands
- `/skill code-review-agent` — Load this skill
- `/run code-review-agent` — Execute workflow
+10
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@@ -0,0 +1,10 @@
# Examples: code-review-agent
## Usage Example
```python
# How to use this skill
# Inputs: Repository diff or code changeset (string), User ID (string)
# Process: Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter → Step 3: The graph processes the input through various steps until completion or human approval is needed
# Outputs: Review result (dictionary containing messages, issues, etc.)
```
+24
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@@ -0,0 +1,24 @@
{
"name": "code-review-agent",
"version": "1.0.0",
"goal": "Automate code review process using a multi-step workflow with human-in-the-loop approval.",
"inputs": [
"Repository diff or code changeset (string)",
"User ID (string)"
],
"steps": [
"Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`",
"Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter",
"Step 3: The graph processes the input through various steps until completion or human approval is needed"
],
"outputs": [
"Review result (dictionary containing messages, issues, etc.)"
],
"failure_modes": [
"Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention."
],
"confidence": 0.95,
"explanation": "This workflow is reusable for any code review process that requires a multi-step analysis with human-in-the-loop approval.",
"source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
"score": 1.0
}
@@ -1,4 +1,4 @@
# Tests: agent-supervisor
# Tests: code-review-agent
## Test Checklist