Development workflow management system with memory tracking
Project description
Prompt Manager
A powerful AI-assisted workflow management system with advanced task tracking and debugging capabilities.
Features
- Memory Bank System: Maintains perfect documentation and project context
- Task Tracking: Advanced task management and progress monitoring
- Debugging Tools: Comprehensive debugging and troubleshooting support
- GitHub Integration: Seamless integration with existing GitHub repositories
- IDE Support: VSCode integration for enhanced development workflow
- bolt.new Integration: AI-powered task generation for web applications
- LLM Enhancement: Autonomous code improvement and pull request generation
Installation
You can install the latest release (v0.3.0) using pip:
pip install prompt-manager==0.3.0
Or download directly from GitHub releases:
# Clone the repository
git clone https://github.com/tosin2013/prompt-manager.git
cd prompt-manager
# Checkout the latest release
git checkout v0.3.0
# Install in editable mode
pip install -e .
Quick Start
Basic Task Management
from prompt_manager import PromptManager
# Initialize project
pm = PromptManager("your_project_name")
# Add a new task
task = pm.add_task(
name="implement-feature",
description="Add new feature X",
details="Implementation requirements..."
)
# Track progress
pm.update_progress(
task_name="implement-feature",
status="in_progress",
details="Completed initial implementation"
)
Generate Web Development Tasks
# Generate structured development tasks with bolt.new
tasks = pm.generate_bolt_tasks(
project_name="My Web App",
framework="Next.js" # Optional, defaults to Next.js
)
# List generated tasks
for task in tasks:
print(f"{task.name} - Priority: {task.priority}")
Using LLM Enhancement
from prompt_manager import LLMEnhancement
# Initialize LLM Enhancement
llm = LLMEnhancement(memory_bank)
# Start learning session
llm.start_learning_session()
# Generate code improvements
suggestions = llm.generate_suggestions()
# Create pull request
pr = llm.suggest_pull_request(
changes=[{"path/to/file.py": "new content"}],
title="Improve code structure",
description="Enhance modularity and readability"
)
# Submit pull request
success, message = llm.create_pull_request(pr)
Using the CLI
# Initialize a new project
prompt-manager init "my-web-app"
# Generate bolt.new tasks
prompt-manager generate-bolt-tasks "Create a blog with authentication" --framework Next.js
# List all tasks
prompt-manager list-tasks
Documentation
Development
- Clone the repository:
git clone https://github.com/tosin2013/prompt-manager.git
cd prompt-manager
- Install dependencies:
pip install -r requirements.txt
- Run tests:
pytest tests/
Release Process
Releases are automated via GitHub Actions. To create a new release:
- Update version in
setup.py - Create and push a new tag:
git tag -a v1.0.0 -m "Release v1.0.0"
git push origin v1.0.0
The GitHub Action will automatically:
- Run tests across Python versions
- Build the package
- Create a GitHub release
- Upload build artifacts
Latest Release (v0.3.0)
What's New
- LLM Enhancement: Autonomous code improvement and pull request generation
- bolt.new Integration: Generate structured development tasks for web applications
- Enhanced Task Management: Improved task tracking and organization
- Memory Bank Updates: Better context management for web development
- New Documentation: Comprehensive guide for bolt.new features and LLM Enhancement
Breaking Changes
None
Bug Fixes
- Improved task persistence
- Enhanced test coverage
- Fixed CLI command handling
- Improved error handling in LLM Enhancement
Contributing
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Built with Cline integration
- Inspired by AI-assisted development workflows
- Powered by bolt.new for web development task generation
- Enhanced by LLM-driven code improvements
Project details
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