shellwise 0.3.0

Local AI terminal co-pilot — understands plain English, executes safely

shellwise

Local AI terminal co-pilot. Understands plain English and shell commands alike — and executes them safely.

By eulogik · Website · GitHub

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  shellwise v0.3.0  —  your local AI terminal co-pilot

Features

• Smart command routing — known commands run directly; natural language goes to AI
• Never exits unexpectedly — sw mode stays alive until you type exit or Ctrl+D
• Safety tiers — read runs instantly; write asks Y/n; critical is locally blocked
• TUI detection — vim, ssh, top etc. prompt to exit sw mode; ls, du, find run inline
• Context-aware — knows your OS, distro, package manager, cwd, git branch, and directory contents
• Fully offline — ships with bundled llama-server binary, no compilation, no Visual Studio, no Ollama required
• Response caching — repeated queries served from cache with smart TTL
• Undo log — write/critical commands logged to ~/.shellwise/undo.log
• History — all executed commands saved to ~/.shellwise/history
• Explain mode — break down what any command does
• File-safe logging — concurrent writes protected with file locking

---

Install

# 1. Install (one command, no extras needed)
pip install shellwise

# 2. First run downloads the model automatically (~400 MB, one time)
sw

# 3. Optional: install shell integration
sw install-shell

That's it. shellwise ships with a bundled llama-server binary for macOS (arm64 + x64), Linux (x64), and Windows (x64). No Visual Studio, no CMake, no separate installer.

The model (qwen2.5-0.5b-instruct-q4_k_m.gguf) downloads to ~/.shellwise/models/ on first use, with resume support if the download is interrupted.

Alternative backends

If you'd rather use a different inference stack:

# Pure-Python backend (no bundled binary needed, but requires C++ build on some platforms)
pip install "shellwise[cpu]"

# GPU (CUDA) — much faster on systems with a GPU
CMAKE_ARGS="-DGGML_CUDA=on" pip install "shellwise[gpu]"

# Or use Ollama (no extra Python deps, separate daemon)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2:0.5b
pip install shellwise   # without [cpu]
sw

shellwise picks the first available backend in this order: bundled binary → llama-cpp-python → Ollama.

---

Usage

sw — interactive mode

$ sw

  shellwise v0.3.0  —  your local AI terminal co-pilot

  How to use:
  · type any shell command        → runs directly
  · type in plain English        → AI generates & runs it
  · prefix with ai               → force AI mode
  · sw --explain <cmd>           → explain a command
  · type exit or Ctrl+D          → leave sw mode

  ~/projects (main) sw ›

In sw mode:

• ls -la → runs directly, shows output, stays in sw mode
• find large files over 500MB → AI generates command, executes inline
• create and open a readme in vim → AI detects interactive, asks to exit sw mode
• --explain "find . -name '*.log' -mtime +7" → explains the command inline
• ai ls -la → forces AI mode even for known commands
• exit or Ctrl+D → leave sw mode

One-shot

sw "show disk usage by folder"
sw "find all python files modified today"
sw ls -la                           # shell passthrough
sw --explain "awk '{print \$2}'"    # explain a command
sw --dry-run "clean up docker"      # preview without running
sw --history                        # recent command history
sw --model phi3:mini "..."          # use a bigger model
sw install-shell                    # install shell integration

---

Safety tiers

Type	Colour	Behaviour
read	white	Runs immediately, streams output
write	yellow ⚠	Shows impact → Y/n confirmation
critical	red ✖	Locally blocked — never executes
interactive	blue	Asks to exit sw mode → runs in full terminal

Local safety classifier

shellwise independently classifies every command (even AI-generated ones):

• rm -rf / → BLOCKED locally, even if AI suggests it
• dd if=/dev/zero of=/dev/sda → BLOCKED
• Fork bombs, sudo rm, mkfs on disks → all BLOCKED
• ls, cat, grep, du → read (auto-execute)
• mkdir, cp, mv, touch → write (confirm first)

Example — write command

  $ rm -rf ./node_modules
  ⚠ impact  Permanently deletes the node_modules directory in the current folder.

  run this? [Y/n]

Example — blocked command

  $ rm -rf /
  ██ BLOCKED  rm -rf /
  This command is locally blocked for safety.
  reason: deletes root filesystem

Example — interactive command

  $ vim README.md

  ⬛ sw mode  'vim' is an interactive terminal app.
  This command will take over the terminal.
  exit sw mode and run it? [Y/n]

---

Files

Path	Contents
~/.shellwise/models/	Downloaded GGUF model
~/.shellwise/history	Tab-separated command history
~/.shellwise/undo.log	Write/critical commands with impact notes
~/.shellwise/cache.jsonl	Cached AI responses with TTL
~/.shellwise/config.json	User configuration

---

Configuration

Create ~/.shellwise/config.json:

{
  "model": "qwen2.5-0.5b-instruct-q4_k_m.gguf",
  "timeout": 120,
  "cache_enabled": true,
  "show_banner": true,
  "color": true
}

Or use environment variables:

• SHELLWISE_MODEL — override model file
• SHELLWISE_TIMEOUT — request timeout in seconds
• NO_COLOR — disable colored output
• SHELLWISE_NO_BANNER — hide banner in interactive mode

---

Models

Model	Size	RAM	Quality
qwen2.5-0.5b-instruct-q4_k_m.gguf	~400 MB	~600 MB	default, fast
phi3:mini (via Ollama)	2.2 GB	~2.5 GB	better on complex tasks

To use a different model:

sw --model my-model.gguf "..."

Place custom .gguf files in ~/.shellwise/models/.

---

Project structure

shellwise/
├── shellwise/
│   ├── __init__.py      version
│   ├── __main__.py      CLI entry point, sw mode, one-shot, flags
│   ├── ai.py            system prompt, command routing, TUI detection
│   ├── classifier.py    local safety classification (READ/WRITE/CRITICAL)
│   ├── cache.py         response cache with TTL
│   ├── config.py        configuration loader
│   ├── core.py          shared processing loop
│   ├── display.py       terminal output, banner, colors, confirmations
│   ├── executor.py      command execution, cd handling, history/undo logging
│   └── model.py         llama-cpp-python backend, auto-download, Ollama fallback
├── tests/
│   ├── test_ai.py
│   ├── test_cache.py
│   ├── test_classifier.py
│   ├── test_core.py
│   ├── test_display.py
│   ├── test_executor.py
│   └── test_model.py
├── pyproject.toml
└── README.md

---

Running tests

pip install -e ".[test]"
pytest              # run all tests
pytest -v           # verbose output
pytest tests/test_classifier.py  # run specific module tests

---

Environment variables

Variable	Effect
NO_COLOR	Disables colored output
SHELLWISE_MODEL	Override model file
SHELLWISE_TIMEOUT	Request timeout (seconds)
SHELLWISE_NO_BANNER	Hide interactive banner