Search Results for "input-output model"

CLI tool to process files and output the code blocks along with explanation, using various configuration options like API key, base URL, model name, and temperature.

Kaggle CLI is Kaggle’s official command-line interface for interacting with the Kaggle platform from a terminal. It lets users authenticate, search resources, download files, submit competition entries, manage datasets, work with models, run notebooks, and read discussion content without relying only on the web interface. The tool is useful for data scientists who want to automate Kaggle workflows inside scripts, CI jobs, notebooks, or reproducible local environments. It supports both...

mcp-cli is a lightweight, Bun-based command-line interface (CLI) tool that simplifies how developers discover, inspect, and interact with Model Context Protocol (MCP) servers from the terminal. The Model Context Protocol is an emerging open standard for connecting language models and external tools, and mcp-cli helps mitigate common pain points like context window bloat by dynamically discovering MCP servers and reducing token load during interactions. It’s designed to be fast and shell-friendly, compiling to a single standalone binary and producing machine-readable output (e.g., JSON) that plays well with other CLI tooling like jq and standard piping. mcp-cli supports both stdio and HTTP-based MCP servers, connection pooling with a lazy-spawn daemon to keep connections warm, and flexible tool filtering via easy configuration options.

jevgrep is a command-line code discovery tool designed to help AI coding agents understand unfamiliar repositories faster. Users ask a question about what a codebase does instead of manually crafting text or symbol searches. The tool evaluates folders, files, and declarations with Jev to identify likely relevant areas. Its response includes file suggestions, reading leads, and verbatim source excerpts in one stdout result. An accompanying Agent Skill teaches tools such as Claude Code, Codex,...

...It supports configuration via environment variables or config files so you can set your API key, preferred model, message style, and more. There are also options to preview, edit, or regenerate the AI-proposed message before actually committing, giving developers control instead of blindly trusting the output. Because it’s published as an npm package and even as a Homebrew formula, installation is straightforward across platforms.

LM Studio CLI (lms) is a command-line tool designed to help developers manage and interact with LM Studio directly from the terminal. Built with lmstudio.js, it provides a streamlined interface for controlling local language models and API server operations. The CLI ships with LM Studio 0.2.22 and newer, making it easy to manage models without additional complex setup. With simple subcommands, users can check system status, start or stop the local server, and monitor logs in real time. It...

DevProjex turns any folder or Git repository into clean, token-efficient context for AI assistants and coding agents, and shows exactly what leaves your machine before you send it. Use it as a desktop app (GUI), a keyboard-first terminal workspace (TUI), a scriptable CLI for CI, or a read-only MCP server for Claude Code, Cursor, and other agents. An open-source alternative to Repomix, gitingest, and code2prompt, with a visual file tree and live preview. Smart Ignore drops build output,...

Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat suitability in various discharge scenarios in a simulated river reach. ...

...A review option allows users to edit generated messages before committing. Dry-run mode displays the suggested message without modifying the repository. Additional verbosity controls adjust terminal output, while installation options include downloadable binaries, shell scripts, and Arch Linux packages.