Prompt Pack: Streamlined Code-Sharing for ChatGPT

March 14, 2025 · View on GitHub

Quick Summary
prompt-pack is a Python CLI tool that recursively scans your project files, applies flexible accept/deny filters, and bundles the selected files into a well-formatted text block which is copied to your clipboard. This text block can then be pasted directly into ChatGPT (or other AI platforms) when you want the AI to review or modify your code.

1. Why Use Prompt-Pack?

1.1. Automate Code Sharing with ChatGPT

If you’ve ever tried to share multiple files with ChatGPT for debugging or refactoring, you know how tedious it can be to manually copy dozens of files, rename them, and ensure proper formatting with backticks. prompt-pack automates this process:

  • Recursively crawls your project directories,
  • Filters out files or folders you don’t want (e.g., logs, temporary folders, large files),
  • Generates a single text block with the selected files neatly labeled,
  • Copies the entire block to your clipboard with proper triple-backtick formatting.

With one command, you can paste a clean, organized codebase into ChatGPT for review or suggestions.

1.2. Save Time & Reduce Errors

Manually copying file contents is prone to mistakes and inconsistencies (missing files, wrong versions, or forgetting a bracket). Using prompt-pack:

  • Ensures consistent naming and structure,
  • Prevents accidental inclusion of sensitive or irrelevant files,
  • Keeps your prompt well-structured so ChatGPT can parse each file correctly.

1.3. Flexible Configuration

prompt-pack is controlled by a .env file, where you can define:

  • Which folders to scan,
  • Which files to accept or deny with regex patterns,
  • Language mappings for syntax highlighting (e.g., .py → python),
  • Maximum file size to avoid copying huge binaries.

By adjusting these settings, you can finely tune exactly what code gets packaged each time.

2. Key Features

  • Recursive Filtering: Skip entire folder subtrees if they don’t match your folder accept/deny regex.
  • File-Level Filtering: Include/exclude files by extension, name patterns, or other regex rules.
  • Clipboard Integration: Automatically copy the final text to your system’s clipboard.
  • Language Mappings: Insert the right triple-backtick language hint (e.g., ```python) for ChatGPT code formatting.
  • Easy Setup: Install via setup.py or use requirements.txt.
  • Cross-Platform: Works on Linux, macOS, Windows (Python 3.7+ required; may need xclip on Linux).

3. Installation

Option A: Using setup.py

  1. Make sure you have Python 3.7+ installed.
  2. In the project directory (where setup.py is located), run: 
    pip install .
  3. Now you can use the command prompt-pack from anywhere in this environment.

Option B: Using requirements.txt

  1. (Optional) create a virtual environment: 
    python -m venv venv
source venv/bin/activate
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run the CLI directly: 
    python promptpack/main.py --env .env --mode list
    or 
    python promptpack/main.py --env .env --mode copy

4. Configuration

prompt-pack reads a .env file, for example:

FOLDERS_TO_SCAN="/my/codebase,/another/folder"

FOLDER_DENY_REGEX_1="^temp.*"
FOLDER_ACCEPT_REGEX_1=".*"

FILE_DENY_REGEX_1=".*\\.log$"
FILE_ACCEPT_REGEX_1=".*\\.py$"
FILE_ACCEPT_REGEX_2=".*\\.txt$"

LANG_MAPPING="py=python,js=javascript"
MAX_FILE_SIZE="500000"
  • FOLDERS_TO_SCAN: comma-separated list of base folders.
  • FOLDER_DENY_REGEX_* / FOLDER_ACCEPT_REGEX_*: which folders to skip or include.
  • FILE_DENY_REGEX_* / FILE_ACCEPT_REGEX_*: which files to skip or include.
  • LANG_MAPPING: associate file extensions with code languages (py=python).
  • MAX_FILE_SIZE: ignore or limit oversized files.

5. Usage

After installing and configuring:

  1. list mode – enumerates the final set of files: 
    prompt-pack --env .env --mode list
    Output might look like: 
    main.py
utils/helpers.py
...
  2. copy mode – copies all accepted files into a single prompt-friendly text block: 
    prompt-pack --env .env --mode copy

    • Each file is labeled and wrapped in triple backticks:

      main.py:

```python
# content of main.py

    • The entire block is placed on your clipboard.

6. Typical Workflow with ChatGPT

  1. Configure your .env once (indicate which folders & files you want to share).
  2. Run prompt-pack --mode copy.
  3. Go to ChatGPT (or another AI tool), paste the resulting prompt:
    • Your files each have their own fenced code block, making it easy for GPT to parse them.
  4. Ask ChatGPT for improvements, fixes, or refactoring.
  5. Profit from GPT’s suggestions without manual copy/paste hassles!

7. Example Scenarios

  • Refactoring a Python Project: Accept all .py files, ignore logs, large data, or virtual environment folders.
  • Sharing Partial Code: Deny certain sensitive files or large binaries via deny regex.
  • Multi-Language Repos: Use LANG_MAPPING to properly highlight .js, .py, .ts, etc.

8. Troubleshooting

  • If copying to clipboard fails on Linux, ensure xclip or xsel is installed, or check pyperclip docs.
  • If a file is too large, a placeholder (# [File too large, skipping content]) is inserted.
  • For very large codebases, you might exceed ChatGPT input limits – refine your accept/deny filters accordingly.

9. Using Prompt-Pack as an MCP Server

Prompt-Pack can be used as an MCP server, allowing direct integration with MCP-compatible AI clients like Claude Desktop, Cursor, and more.

9.1. Setup with MCP

  1. Make sure you have uv installed (a fast Python package installer and resolver)
  2. Configure your MCP client (like Claude Desktop) with the following settings:
{
  "mcpServers": {
      "prompt-pack": {
         "command": "uv",
         "args": ["run", "<local_dir_path>/prompt-pack/mcp-server/mcp_server.py"]
      }
  }
}

Replace <local_dir_path> with the actual path to your prompt-pack installation.

9.2. MCP Tools

When configured, your AI assistant will have access to these tools:

  • bundle_code: Bundles source code files from a specified directory using the same powerful filtering capabilities as the CLI
  • list_files: Lists files in a directory using specified filter patterns

9.3. Example Usage with Cursor

  1. Configure the MCP server in Cursor settings: Settings -> MCP -> Add new MCP Server
  2. Select command type and provide the following command:
uv run <local_dir_path>/prompt-pack/mcp-server/mcp_server.py
  1. Replace <local_dir_path> with the actual path to your prompt-pack installation.

Once configured, you can ask your AI assistant in agent mode to:

  1. List files in a directory: "Using prompt pack tool list out all adoc files in the current project"
  2. Bundle code for analysis: "Use prompt-pack mcp server tool to review all Rest Controller classes and generate list of available API endpoints"

The AI will use the MCP tools to access your files directly, with the same powerful filtering capabilities as the CLI version.

10. License

You can freely use or modify prompt-pack. Contributions are welcome!