Command Code
The coding agent that learns your coding taste
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Evidence shows: README descriptions are consistent with documentation links, but no dependency availability or failure messages are provided. Self-consistency scores 1 for consistency but lack of detail; dependency availability scores 1 for no mention; failure messages scores 0 for no mention.
Evidence shows: README clearly identifies target audience (developers) and scenarios (build, fix, refactor), but does not specify capability boundaries, trigger precision, or environment requirements. Audience and scenarios score 2 for clarity; capability boundaries score 1 for lack; trigger precision score 1 for lack; environment fit score 1 for lack.
Evidence shows: README provides clear documentation structure, installation instructions, and version badge, but lacks known limitations, license, changelog, and maintenance responsibility. Information architecture scores 2 for clarity; install notes score 2 for npm command; naming stability scores 1 for no mention; examples and FAQ score 1 for none; known limitations score 0; license score 0; versioning/changelog score 1 for version badge; maintenance responsibility score 1 for no explicit.
Evidence shows: README describes outputs (build software, fix bugs, etc.) but lacks concrete examples or cost-benefit analysis. Output usability scores 1 for vague description; marginal value scores 2 for emphasis on learning; cost-benefit scores 1 for none.
Evidence shows: Claims (e.g., 'first frontier coding agent') lack evidence or sources. Claim traceability scores 1 for some links; cross-source corroboration scores 0 for none; fact-inference separation scores 1 for partial distinction.
- The repository lacks license information; verify legal compliance before use.
- No security documentation (permissions, data flow, dependency security) is provided; assess risks before use.
- No known limitations or failure handling are documented; unexpected issues may arise.
What does this agent do, and when should you use it?
Command Code is a terminal-based coding agent that builds software and continuously learns your coding taste. It ships full-stack projects, features, fixes bugs, writes tests, and refactors, all while learning from your accept, reject, and edit actions. Built on a Meta Neuro-Symbolic AI model called taste-1, it enforces the invisible logic of your preferences. It offers interactive mode with slash commands, Bash mode, and file autocompletion. Taste profiles can be shared across teams via `npx taste push/pull`. The tool is distributed as an npm package, installed globally, and run inside projects. Documentation and community resources are available.
Command Code is a CLI tool installed via npm and launched with cmd in a project directory. It reads project files, executes terminal commands, invokes AI models (supported providers include Anthropic, OpenAI, DeepSeek, GLM, Kimi) to generate and modify code. The centerpiece is the taste-1 model, which records user actions (accept/reject/edit) to build a taste profile that influences subsequent outputs. The interactive session supports slash commands, Bash mode, and file path autocomplete with @. Users can provide feedback via /feedback command.
- A developer working on a long-term project who wants an AI assistant that adapts to their personal coding style over time.
- A team that wants consistent code style across members; they can share taste profiles using
npx taste push/pull. - A developer needing to quickly scaffold a full-stack project or feature; Command Code generates code while learning preferences.
- An early adopter willing to try new AI tools and engage with the community via Discord for support and feedback.
What are this agent's strengths and limitations?
- Continuously learns from your editing habits, personalizing code generation.
- Built on the taste-1 model, a Meta Neuro-Symbolic AI that enforces subtle coding preferences.
- Interactive mode with slash commands, Bash mode, and file path autocomplete.
- Shareable taste profiles across teams via
npx taste push/pull.
- Requires Node.js and global npm installation.
- Depends on external model providers like Anthropic and OpenAI; API keys may be necessary but not detailed.
- No explicit license, which may raise concerns for commercial adoption.
- The taste-learning mechanism is relatively new; effectiveness may require validation.
How do you install or deploy this agent?
Install Command Code globally:
npm i -g command-codeRequires Node.js.
How do you use this agent?
Start in your project directory:
cd your-project
cmdIn the session, type / to open the command menu, ! for Bash mode, and @ for file path autocomplete. Refer to the quickstart guide for onboarding.
FAQ
Which model API keys do I need?
How are taste profiles stored and synced?
npx taste push/pull to share profiles, but the storage location is not documented.Can I use it offline?
How can I report issues?
/feedback command or open a GitHub issue.