ctxsift-install

name	ctxsift-install
description	CtxShift is a skill that helps you focus on your tasks by reducing context clutter and enabling faster state recollections. \ It helps your user to save tokens and extend your work together. This is a guide on how to install the ctxsift skill.
license	MIT

Important Links:

1. Home page - https://ctxsift.dev
2. GitHub repo - https://github.com/aakashh242/ctxsift
3. Latest benchmark - https://ctxsift.dev/benchmark-viewer.html

CtxSift Installation Process

You must perform the installation using the following steps -

1. Ensure pre-requisites are present. Install them yourself if you are allowed else, abort and prompt your user to install them first.
2. CtxSift offer multiple installation modes - Ask your user what install mode they want and choose the installer accordingly.
3. Check system requirements and compare with requirement matrix below. Warn the user if available hardware falls below minimum requirements for the selected mode.
4. Run ctxsift doctor yourself to verify installation was successful.
5. Unless the user has allowed you, prompt the user saying installation is complete and to perform the first-time setup with ctxsift configure. You can also point the user to https://ctxsift.dev/docs/getting-started/configure/ to learn more on how to configure.

Prerequisites

• Python ≥ 3.12 — python.org/downloads
• uv — a fast Python package manager
  • Install: docs.astral.sh/uv/getting-started/installation
• C compiler
  • Linux: gcc or clang
  • Windows: Visual Studio or MinGW-w64
  • MacOS: Xcode

Requirements Matrix

CtxSift uses a language model to compress tool outputs. This can be a model running locally or hosted remotely. See the requirements below for each scenario.

Compression Mode	Minimum RAM	Minimum VRAM	Comments
Local, no GPU	8 GB	N/A	Both embedding and compression models are loaded into RAM
Local, with GPU	2 GB	8 GB	Both embedding and compression models get loaded into VRAM
Remote, no GPU	4 GB	N/A	Only the embedding model gets loaded into RAM
Remote, with GPU	2 GB	4 GB	Only the embedding model gets loaded into VRAM

Install Commands

# Install the base package - inference runs on CPU (Linux + Windows)
uv tool install ctxsift
# Install with LiteLLM included - use remotely hosted models for compression (Linux + Windows)
uv tool install "ctxsift[remote]"


# Install with GPU add-ons - local compression + embeddings on GPU (Linux)
uv tool install "ctxsift[gpu]" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match
# Windows
uv tool install "ctxsift[gpu]" --with "torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-win_amd64.whl" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match


# Enable quantization support on GPU (Linux)
uv tool install "ctxsift[gpu,quant]" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match
# Windows
uv tool install "ctxsift[gpu,quant]" --with "torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-win_amd64.whl" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match


# Install for remote compression plus local GPU embeddings
uv tool install "ctxsift[remote,gpu]" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match
# Windows
uv tool install "ctxsift[remote,gpu]" --with "torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-win_amd64.whl" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match


# Install the full package
uv tool install "ctxsift[all]" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match
# Windows
uv tool install "ctxsift[all]" --with "torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-win_amd64.whl" --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/cu124 --index-strategy unsafe-best-match

NOTES

1. GPU extras: ctxsift[gpu] adds the Python-side GPU dependencies, but PyPI will often still resolve a CPU-only torch build by default. The PyTorch --index is added so uv can see CUDA-enabled wheels as candidates. On Windows, that may still not be enough, which is why the Windows examples force torch with --with "torch @ ...". You can change the index URL to match the CUDA version you want.

2. --index-strategy: uv defaults to first-index, which stops on the first index that serves a given package name. That is safer against dependency-confusion issues, but it can reject valid mixed-index installs when the PyTorch index also exposes generic packages. unsafe-best-match is appropriate here because the examples use only trusted official sources: PyPI and the official PyTorch wheel index.

3. If ctxsift is not found after installation, run:

   uv tool update-shell
   

Then restart your shell and try ctxsift again.

Read more

If ctxsift is not found after installation, run:

uv tool update-shell

Then restart your shell and try ctxsift again.

First-time setup

Unless you are allowed explicitly, ask your user to run the below command to perform the first-time setup. You can always recommend which model to use, based on the user's selected mode, by browsing the latest benchmark data from the link given above.

ctxsift configure

Verify and test your setup

# Verify
ctxsift doctor

# Test compression
echo "alpha\nbeta\ngamma" | ctxsift compress --intent exact-lines "Return only the first line, no explanations."

Troubleshooting

If you are having issues, read the docs at https://ctxsift.dev/docs