ai-pack-cli 0.1.9

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📦 ai-pack

Pack your entire codebase, specific files, or uncommitted changes into a single token-optimized Markdown prompt for LLMs (like ChatGPT, Claude, Gemini).

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[!IMPORTANT] 📋 Default Clipboard Behavior

By default, running aip automatically copies the generated Markdown payload directly to your system clipboard. No files are saved to disk unless you specify the output file using the -o or --output flag.

Note: It also natively supports remote SSH sessions using the OSC 52 clipboard escape sequence.

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⚡ Key Features

• 📋 Clipboard-First: Automatically copies your prompt, ready to paste straight into ChatGPT, Claude, Gemini, or any LLM interface.
• 💀 Skeleton Mode (-s / --skeleton): Drastically saves tokens by stripping function/method bodies, keeping only class structures, signatures, and imports.
• 🎯 Interactive Selector (-i): Choose files interactively via checkbox CLI menu.
• 🌿 Git-Aware (-c / --changed): Automatically pack only modified, staged, or untracked files.
• 🛡️ Gitignore-Respecting: Excludes ignored, temporary, or build files automatically (using git ls-files with manual fallback for non-git folders).
• 💬 Prompt Presets (-p): Instantly prepend pre-configured prompts for code review, bug hunting, or architecture explanations.
• 📊 Token Estimation: Heuristic token counting warns you if your payload exceeds your limit (--max-tokens).

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🚀 Quick Start

Installation

Choose your preferred installation method:

# Option 1: Install official package via pip (Recommended)
pip3 install ai-pack-cli --user

# Option 2: Install via pipx (Isolated environment)
pipx install ai-pack-cli

# Option 3: Install development version directly from GitHub
pip3 install git+https://github.com/iamraydoan/ai-pack.git --user

[!TIP] Skeleton Mode (-s / --skeleton) for non-Python languages: To extract code skeletons for JavaScript, TypeScript, Go, Rust, Java, C#, C++, PHP, Lua, CSS, Swift, and Kotlin, install ast-grep globally:

npm install -g @ast-grep/cli
# or: cargo install ast-grep

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💡 Usage & Common Scenarios

Here are common ways to use ai-pack (using commands aip, ai-pack, or aipack):

1. Pack codebase & copy to clipboard (Default)

aip

2. Pack specific files/folders and save to a file

aip -f src/main.py tests/ -o output.md

3. Pack only uncommitted changes with a code review prompt

aip -c -p review

4. Pack code skeleton to save context window tokens

aip -s -p explain

5. Choose files interactively before packing

aip -i

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⚙️ CLI Flags & Interactions

Reference

Flag	Short	Type	Description
--files	-f	Path(s)	Specific files or directories to pack (space-separated).
--changed	-c	Flag	Pack only modified, staged, or untracked Git files.
--interactive	-i	Flag	Select files interactively via a checkbox menu.
--skeleton	-s	Flag	Strip method bodies, leaving only signatures and imports to save tokens.
--prompt	-p	Choices	Prepend preset: review, bug, or explain.
--output	-o	Path	Save output directly to a file instead of copying to clipboard.
--max-tokens		Number	Count approximate tokens and warn if the limit is exceeded.

🤝 Combining Flags

You can combine flags to narrow down and customize your payload:

• Filter Git changes (-f + -c / --changed): Only packs files that are both modified/uncommitted AND located inside the specified paths.

  aip -c -f src/
  

• Interactive selection with filter (-i + -f / -c): Shows only the filtered list (e.g. only changed files) in the interactive checkbox menu instead of the entire repo.

  aip -i -c
  

• Structure only (-s / --skeleton + any mode): Can be appended to any command to extract skeletons instead of full source code for the selected files.

  aip -i -s
  

• Redirect output (-o vs Default): By default, everything goes to the clipboard. Use -o filename.md to save to a file instead.

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💀 Skeleton Mode Example

Original Code:

def fibonacci(n):
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    sequence = [0, 1]
    while len(sequence) < n:
        sequence.append(sequence[-1] + sequence[-2])
    return sequence

Skeleton Output:

def fibonacci(n):
    ...

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🤝 Contributing

1. Fork the repository.
2. Create your feature branch (git checkout -b feat/amazing-feature).
3. Commit your changes (git commit -m 'feat: add amazing feature').
4. Push to the branch (git push origin feat/amazing-feature).
5. Open a Pull Request.

Give this repository a ⭐ if it helped you pack your code for LLMs!