ifai
🥈SilverIfAI is a cross-platform AI code editor built with Tauri 2.0 and React 19. It enables developers to write, edit, and debug code with AI assistance. The editor integrates with Claude for code suggestions, completions, and explanations. It benefits software development teams by accelerating coding tasks and improving code quality.
8480Updated 2mo ago
Intermediate30min to implementautomation
Saves ~90 min per use
Quick InstallView Source
git clone https://github.com/peterfei/ifai.gitWorks with:
Claude
Overview
About This Skill
IfAI is a cross-platform code editor that combines traditional editing with AI agent orchestration capabilities. The platform coordinates 9+ specialized agents (Explore, Review, Refactor, Test, Doc, Plan, ReAct, Git Commit, Debug) through DAG-based workflows declared in YAML, enabling complex multi-step coding tasks. It features symbol-aware RAG for precise code understanding, Composer 2.0 for parallel multi-file editing with conflict detection, and Shell-level control allowing agents to execute npm, git, and cargo commands autonomously. The Rust-powered backend delivers 120 FPS performance even with thousands of records, while natural language intent routing automatically matches user requests to appropriate agents. Developers benefit from reduced coding friction through agent collaboration, integrated version control workflows, and local-first privacy support for sensitive code.
How to Use
[{"step":"Install IfAI and integrate it with your preferred IDE (e.g., VS Code, Neovim).","tip":"Ensure you have Tauri 2.0+ and React 19 installed for full compatibility. Check the IfAI GitHub repo for setup instructions."},{"step":"Open the code snippet you want to improve in IfAI. Highlight the relevant section or file.","tip":"Use IfAI's multi-cursor support to edit multiple parts of the code simultaneously."},{"step":"Describe the task in the prompt template, specifying the action (refactor/debug), programming language, and coding standard.","tip":"Be specific about the standard (e.g., 'follow PEP 8') to get tailored suggestions. Use placeholders like [ACTION] and [PROGRAMMING_LANGUAGE] directly in the prompt."},{"step":"Review IfAI's suggestions in the editor. Accept, modify, or reject changes directly in IfAI.","tip":"Use IfAI's inline diff view to compare changes before applying them. Enable 'Auto-Apply' for minor fixes."},{"step":"Test the refactored code in your local environment to ensure functionality.","tip":"Run unit tests or manual checks to validate the changes. IfAI can generate test cases for you—ask it to 'write unit tests for this function'."}]
Use Cases
Orchestrate multi-agent workflows for complex refactoring tasks across multiple files
Automate code review, testing, and documentation generation through coordinated agents
Execute git operations with AI safety checks (secret scanning, pre-flight validation)
Parallel code editing with real-time diff preview and conflict resolution
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/peterfei/ifaiRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Use IfAI to [ACTION: refactor/debug/optimize/explain] the following code snippet for [PROGRAMMING_LANGUAGE]. Ensure the changes align with [CODING_STANDARD: e.g., PEP 8, SOLID principles]. Provide a before-and-after comparison with inline comments explaining the improvements. Code snippet: [CODE_SNIPPET]
Example Output
### Code Refactor Request for IfAI
**Original Code (Python - Legacy Data Processing Script):**
```python
def process_data(data):
result = []
for item in data:
if item['status'] == 'active':
temp = item['value'] * 2
result.append(temp)
return result
```
**IfAI Refactored Code (PEP 8 Compliant with Type Hints & List Comprehension):**
```python
def process_data(data: list[dict]) -> list[int]:
"""
Processes a list of dictionaries, filtering for 'active' items and doubling their values.
Args:
data: List of dictionaries with 'status' and 'value' keys.
Returns:
List of processed values.
"""
return [item['value'] * 2 for item in data if item['status'] == 'active']
```
**Key Improvements:**
1. **Type Hints:** Added `list[dict]` and `list[int]` annotations for better IDE support and readability.
2. **List Comprehension:** Replaced the explicit loop with a more Pythonic and performant list comprehension.
3. **Docstring:** Added a clear docstring explaining the function's purpose, arguments, and return value.
4. **Variable Naming:** Removed redundant `temp` variable and improved clarity.
5. **Performance:** List comprehensions are generally faster than explicit loops in Python.
**Testing Notes:**
- The refactored version was tested with the same input: `[{'status': 'active', 'value': 5}, {'status': 'inactive', 'value': 3}]`
- Output: `[10]` (matches original behavior).
- Edge case handled: Empty input list returns `[]`.
**Next Steps:**
- Consider adding error handling for missing keys (e.g., `item.get('status')`).
- Profile the function with large datasets to validate performance gains.Apply to these tools
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