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Shell Sage

Paid

Your intelligent terminal companion, making command-line operations intuitive and safe.

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About Shell Sage

Shell Sage is an AI-powered terminal assistant designed to make command-line operations more intuitive and safe. It interprets natural language requests, translates them into shell commands, and provides context-aware error analysis with automatic fixes for common issues. The tool emphasizes safety by offering interactive confirmation for destructive commands and detecting potentially dangerous operations, such as absolute paths in rm commands. It supports both local and cloud processing modes, allowing users to prioritize privacy with local models or leverage faster performance via cloud APIs.

Key Features

Natural language to command translation
Context-aware error diagnosis and automatic fixes
Interactive safety confirmation for destructive commands
Hybrid local and cloud processing modes
Support for multiple cloud providers: Groq, OpenAI, Anthropic, Fireworks, DeepSeek, OpenRouter
Local processing via Ollama integration with model management
Privacy-first local mode that retains all data on the user's machine

Pros & Cons

Pros
  • Natural language interface lowers the barrier for CLI usage
  • Safety checks help prevent destructive command execution
  • Local mode offers privacy by keeping data on the user's machine
  • Supports a wide range of cloud providers for flexibility
  • Open-source project with a GitHub repository for community contributions
Cons
  • Requires Python 3.8+ and 4GB+ RAM for local model processing
  • Internet connection needed for initial setup and cloud API mode
  • Local model performance may vary based on hardware specifications
  • Free tier limits and pricing model are not publicly specified (contact required)
  • Setup involves multiple steps including cloning a repository and installing dependencies

Best For

Translating natural language requests into shell commands for users unfamiliar with CLI syntaxDiagnosing and fixing command errors with context-aware suggestionsCreating safe backup workflows with automatic file discovery and copyingPreventing accidental data loss by warning about dangerous commands like rm -rfSwitching between privacy-focused local models and faster cloud APIs based on task requirements

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FAQ

How does local mode differ from cloud mode?
Based on available information, local mode processes commands entirely on the user's machine using Ollama, ensuring data privacy but requiring at least 4GB of RAM. Cloud mode uses external APIs (e.g., Groq, OpenAI) for faster responses but requires an internet connection.
Which models are officially supported?
The tool appears to support local models such as llama3:8b-instruct-q4_1, mistral:7b-instruct-v0.3, and phi3:mini-128k-instruct via Ollama. Cloud providers include Groq, OpenAI, Anthropic, Fireworks, DeepSeek, and OpenRouter. The exact list should be verified on the project's GitHub page.
How do I switch between providers?
The tool includes a configuration command (shellsage config --mode ...) and an interactive setup wizard that allows selecting operation mode and provider. Switching providers likely involves re-running the setup or using a config command, but specific steps should be checked in the documentation.
Is Shell Sage free to use?
The tool is open-source and appears to be free for local use. Cloud API mode may incur costs depending on the chosen provider's pricing. The exact pricing model for any premium features is not specified and should be verified by contacting the developer or checking the repository.
What are the system requirements?
The website indicates Python 3.8+ is required for core functionality, 4GB+ RAM is recommended for local models, and an internet connection is needed for initial setup and API mode. These requirements should be confirmed for the specific use case.
Can I use Shell Sage without an internet connection?
Based on available information, local mode can operate without an internet connection after initial setup and model download. Cloud mode requires an active internet connection. The exact offline capabilities should be verified in the documentation.