dirshot 0.2.0

A flexible, high-performance utility for creating project snapshots and searching files with a rich terminal UI.

dirshot 📸

A flexible, high-performance utility for creating project snapshots and searching files, complete with rich visual feedback in your terminal.

dirshot scans your project directories to either create a single, comprehensive text file "snapshot" of your codebase or to search for specific keywords within your files. It's perfect for feeding project context to Large Language Models (LLMs), archiving codebases, conducting security audits, or simply navigating complex projects.

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✨ Key Features

• Rich Terminal Visuals: Powered by rich, dirshot provides a beautiful and informative live view of its progress, including spinners, progress bars, summary tables, and thread activity. It falls back to a simple text-based progress indicator if rich is not installed.
• Powerful Presets: Comes with an extensive list of LanguagePreset and IgnorePreset enums to instantly configure scans for dozens of languages, frameworks, and tools (Python, JavaScript, Go, Rust, Terraform, Docker, etc.).
• Dual Modes:
  • Snapshot Mode: Collate all project files matching your criteria into a single, easy-to-share output file.
  • Search Mode: Hunt for specific keywords in filenames or, optionally, within file contents.
• Highly Customizable: Fine-tune scans by combining presets with manual lists of file extensions, ignore paths, and specific file names to include or exclude.
• Concurrent & Fast: Uses a ThreadPoolExecutor for high-performance file discovery and processing, making scans quick and efficient.
• Detailed Output: Generates an optional file tree, shows detailed scan summaries, and can even approximate token/character counts, which is useful for LLM context limits.

📦 Installation

You can install dirshot directly from PyPI.

Basic Installation (no visual dependencies):

pip install dirshot

Installation with Enhanced Terminal Visuals:

To get the full rich terminal experience, install the rich extra, which adds support for rich.

pip install dirshot[rich]

🚀 How to Use

dirshot is used by importing the generate_snapshot function into a Python script.

Here is a basic example of creating a snapshot of a Python project:

# snapshot_script.py
from src.dirshot import generate_snapshot, LanguagePreset, IgnorePreset
# or 
# from src.dirshot import *

generate_snapshot(
    root_directory=".", # optional
    output_file_name="my_python_project.txt",
    language_presets=[LanguagePreset.PYTHON, LanguagePreset.MARKUP],
    ignore_presets=[
        IgnorePreset.VERSION_CONTROL, # Ignore .git
        IgnorePreset.PYTHON,          # Ignore __pycache__, venv, etc.
        IgnorePreset.IDE_METADATA     # Ignore .vscode, .idea
    ],
    generate_tree=True,
    show_token_count=True
)

To run this, save the code as a Python file (e.g., snapshot_script.py) and execute it from your terminal:

python snapshot_script.py

📋 Examples

The examples.py file in the repository contains many more use cases. Here are a few common ones:

1. Full-Stack Web App (React + Node.js)

This example combines multiple presets to capture a full-stack JavaScript project while ignoring common clutter like node_modules.

generate_snapshot(
    root_directory=".",
    output_file_name="fullstack_js_snapshot.txt",
    language_presets=[
        LanguagePreset.REACT,      # Handles all frontend JS/TS/JSX files
        LanguagePreset.WEB_FRONTEND  # Includes HTML and CSS files
    ],
    ignore_presets=[
        IgnorePreset.NODE_JS,      # Crucial for ignoring node_modules
        IgnorePreset.IDE_METADATA,
        IgnorePreset.BUILD_ARTIFACTS,
        IgnorePreset.VERSION_CONTROL
    ],
    generate_tree=True,
    show_tree_stats=True
)

2. Data Science Project (Python, Notebooks & SQL)

Collate all relevant files from a data analysis or machine learning project.

generate_snapshot(
    root_directory="./data_science_project",
    output_file_name="data_science_snapshot.txt",
    language_presets=[
        LanguagePreset.PYTHON,
        LanguagePreset.DATA_SCIENCE_NOTEBOOKS, # .ipynb
        LanguagePreset.SQL,
        LanguagePreset.MARKUP # Include READMEs
    ],
    ignore_presets=[
        IgnorePreset.PYTHON,             # Ignores venv, __pycache__, etc.
        IgnorePreset.JUPYTER_NOTEBOOKS,  # Ignores .ipynb_checkpoints
        IgnorePreset.IDE_METADATA,
        IgnorePreset.VERSION_CONTROL
    ]
)

3. Search for Secrets or API Keys

Use "Search Mode" to perform a security audit. This example intentionally omits the SECRET_FILES ignore preset to ensure .env files are searched.

generate_snapshot(
    root_directory=".",
    output_file_name="secrets_audit_results.txt",
    search_keywords=["password", "secret_key", "api_key", "token"],
    language_presets=[LanguagePreset.CONFIGURATION],
    search_file_contents=True,
    ignore_presets=[
        IgnorePreset.VERSION_CONTROL,
        IgnorePreset.NODE_JS,
        IgnorePreset.BUILD_ARTIFACTS
        # Deliberately not ignoring SECRET_FILES
    ]
)

⚙️ API Reference

The generate_snapshot() function accepts the following parameters:

Parameter	Type	Default	Description
root_directory	str	"."	The starting directory for the scan.
output_file_name	str	"project_snapshot.txt"	The name of the file to save the results to.
search_keywords	Optional[List[str]]	None	If provided, switches to Search Mode. Otherwise, runs in Snapshot Mode.
language_presets	Optional[List[LanguagePreset]]	None	A list of LanguagePreset enums for common file types (e.g., LanguagePreset.PYTHON).
ignore_presets	Optional[List[IgnorePreset]]	None	A list of IgnorePreset enums for common ignore patterns (e.g., IgnorePreset.NODE_JS).
file_extensions	Optional[List[str]]	None	A manual list of file extensions to include (e.g., [".py", ".md"]).
ignore_if_in_path	Optional[List[str]]	None	A manual list of directory or file names to exclude.
ignore_extensions	Optional[List[str]]	None	A manual list of file extensions to explicitly ignore (e.g., [".log", ".tmp"]).
search_file_contents	bool	True	In Search Mode, search for keywords within file contents.
generate_tree	bool	True	Include a file tree of the matched files at the top of the output.
show_tree_stats	bool	False	Display file and directory counts in the generated tree.
show_token_count	bool	False	Display an approximated token/character count in the summary and output file.
exclude_whitespace_in_token_count	bool	False	If True, removes whitespace before counting tokens for a more compact count.
max_workers	Optional[int]	CPU count + 4	The maximum number of worker threads for concurrent processing.
read_binary_files	bool	False	If True, the content search will attempt to read and search through binary files.

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/your-feature-name).
6. Open a pull request.