We’re excited to announce the first public release of clip4llm (v0.0.1), a lightweight command-line tool designed to make working with Large Language Models faster and more efficient. Published on September 14, 2024, this initial release marks the beginning of a project built for developers who frequently paste file contents into LLMs like ChatGPT for code review, debugging, or assistance.

What’s New

clip4llm solves a simple but painful problem: manually copying individual files from your project directory and pasting them into an LLM prompt. With v0.0.1, you can now gather multiple file contents with a single command and have them automatically copied to your clipboard, ready to paste.

Key features in this initial release:

Smart File Selection

  • Use wildcard patterns like *.md or *.yaml to grab all matching files at once
  • Hidden files are excluded by default, but you can explicitly include them (like .env files) with the --include flag
  • Binary files are automatically detected and skipped—no more accidentally copying compiled binaries or images

Flexible Configuration

  • Customize file size limits per file (default 32KB) to respect LLM context windows
  • Set persistent defaults using a .clip4llm config file in your project or home directory
  • Choose custom delimiters to mark file boundaries for different LLM requirements

Cross-Platform Support Pre-built binaries are available for Windows, Linux, and macOS (both Intel and Apple Silicon), so you can start using clip4llm immediately without building from source.

Why It Matters

Working with LLMs often means sharing code context. Before clip4llm, that workflow looked like: navigate to a file, open it, select all, copy, switch to the browser, paste, add comments, repeat for each additional file. For complex debugging sessions or code reviews involving multiple files, this becomes tedious and error-prone.

clip4llm eliminates that friction. A single command gathers your project’s relevant files, formats them cleanly, and copies everything to your clipboard in one go. The tool handles the repetitive parts so you can focus on what actually matters—crafting better prompts and getting better answers from your LLM.

The built-in safeguards help prevent common pitfalls: binary file detection keeps irrelevant data out of your prompts, size limits protect against accidentally overwhelming context windows, and verbose mode gives you visibility into what’s being included or excluded.

Getting Started

Installation

Download the appropriate pre-built binary for your platform from the v0.0.1 release page:

  • macOS: Choose darwin-amd64 (Intel) or darwin-arm64 (Apple Silicon)
  • Linux: Choose linux-amd64, linux-386, or linux-arm64
  • Windows: Choose windows-amd64 or windows-386

Extract the archive and move the clip4llm binary to a directory in your PATH:

tar -xzf clip4llm-v0.0.1-darwin-amd64.tar.gz
mv clip4llm /usr/local/bin/

Build from Source

If you prefer building from source (requires Go 1.23.1 or later):

git clone https://github.com/UnitVectorY-Labs/clip4llm.git
cd clip4llm
go build -o clip4llm
mv clip4llm /usr/local/bin/

First Steps

Navigate to any project directory and run:

clip4llm

This copies all non-binary, non-hidden files under 32KB to your clipboard. Try pasting into your LLM of choice—you should see file contents separated by delimiters for easy reference.

For more control, check out these examples:

# Include hidden .env files but skip markdown
clip4llm --include="*.env" --exclude="*.md"

# Allow larger files (64KB per file)
clip4llm --max-size=64

# See what clip4llm is doing
clip4llm --verbose

AI Transparency Note: This post was AI-generated using the model unsloth/Qwen3.5-122B-A10B-GGUF:Q4_K_M. It references the clip4llm repository and the v0.0.1 release published on September 14, 2024. Written by release-storyteller.