Project Indexer is a simple script designed to index the locations of classes, files, and other components within a project. This indexing allows Large Language Models (LLMs) to quickly understand the project's structure, enabling D.R.Y. (Don't Repeat Yourself) coding by preventing redundant object creation and avoiding full project scans.

By providing an organized index, Project Indexer enhances the LLM's understanding from the outset. When combined with a well-structured design document containing patterns and examples, it facilitates efficient onboarding for new tasks or ongoing development in an active codebase.

The tool currently supports both C# and Python files, with specialized regex patterns for each language's syntax. The index includes:

• For C#: classes, structs, interfaces, enums, and methods
• For Python:
  • Classes (including decorated classes)
  • Functions (including decorated functions)
  • Methods (instance, class and static methods)
  • Imports (absolute and relative)
  • Docstrings (as metadata)

• Reduces unnecessary LLM scanning: Saves token usage compared to constant back-and-forth querying.
• Prevents hallucinations: Helps LLMs recognize what exists in the codebase.
• Speeds up development: Ideal for large projects with many files and classes.
• Compatible with multiple LLMs, including:
  • RooCode - the Awesome VSCode AI Developement Extension.
  • Gemini (All)
  • Sonnet (All)
  • o3-mini and o3-mini-high
  • Deepseek V3.1 & Deepseek R1
  • GPT models
  • Grok

• Creates a .json file listing class names, methods and locations
• Multi-language support: Works with both C# and Python codebases
• Tree-sitter parsing: More accurate than regex for complex syntax cases
• Lightweight: Only indexes names and locations, not full class properties or implementations
• Supports integration with RooCode: In some cases, you can configure Code/Architect mode to frequently refer to ProjectIndex.json. You may also set it to run periodically upon reaching milestones or creating new tasks

Note: This tool was built for personal use due to managing a VS Solution with 5 projects and over 600 classes/methods.

The project uses a modular parser system with Tree-sitter for accurate syntax parsing:

• parser/__init__.py: Main parser interface and Tree-sitter grammar initialization
• parser/csharp_parser.py: Handles C# specific parsing using Tree-sitter
• parser/python_parser.py: Handles Python specific parsing using Tree-sitter

Each parser implements:

• File extension detection
• Tree-sitter based parsing with language grammars
• Common output format (JSON)

The project now uses Tree-sitter for more accurate parsing:

• Pre-loaded grammars for Python and C# at startup
• Faster parsing by avoiding regex pattern matching
• More reliable extraction of code structures
• Better handling of complex syntax cases

Required dependencies (automatically installed via requirements.txt):

• tree-sitter==0.24.0
• tree-sitter-python==0.23.6
• tree-sitter-c-sharp==0.23.1

1. Drop Project_Indexer.py into the root of your project

2. Configure the root directory:

   • Open Project_Indexer.py
   • On line 54, set root_directory to your project's root path
   • Example for Windows: "C:\\MyProject"
   • Example for Mac/Linux: "/Users/name/MyProject"

3. Run the script via command line:

# Using --path argument to specify project directory
python Project_Indexer.py --path /path/to/your/project
# Using --imports to specify extracting imported libraries/methods in each file (only supported for python right now)
python Project_Indexer.py --path /path/to/your/project --imports
# Without arguments (uses hardcoded path in script)
python Project_Indexer.py

1. The script will generate ProjectIndex.json.

2. Direct your LLM to read ProjectIndex.json for efficient project awareness.

1. This is a highly specialized indexer for C# files only. It also reads and indexes .razor files for Blazor projects.
2. It uses the PythonNET library to interface with Roslyn analyzers to "walk the tree," resulting in a highly compressed ProjectIndex.json file.
3. Place the script in the root directory of your project.
4. Run the following command to generate the index: python cSharpIndexer.py
5. If Roslyn dependencies are missing, the script will attempt to install them automatically. Manual installation may be required in some cases.
6. Pull requests and contributions are welcome — this tool is highly experimental.

I hope this is useful.

Happy coding!

Feel free to fork the repository and submit pull requests for improvements!

This project is licensed under the MIT License.