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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
9 changed files with 117 additions and 117 deletions
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---
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
@@ -1,6 +0,0 @@
# Commands: agent-creation-and-management
## Available Commands
- `/skill agent-creation-and-management` — Load this skill
- `/run agent-creation-and-management` — Execute workflow
@@ -1,10 +0,0 @@
# 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
```
@@ -1,27 +0,0 @@
{
"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
}
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---
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
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# Commands: code-review-agent
## Available Commands
- `/skill code-review-agent` — Load this skill
- `/run code-review-agent` — Execute workflow
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# 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.)
```
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{
"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-creation-and-management
# Tests: code-review-agent
## Test Checklist