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Web and mobile front-end for terminal coding agents — Claude Code, Cursor CLI, Codex, and Gemini-CLI. Drive live sessions from a browser with an integrated shell, file/Git explorers, and a plugin system. Self-host or use the managed Cloud option.
Desktop app that runs many CLI coding agents — Claude Code, Codex, Cursor, Gemini — in parallel, each in its own git worktree and branch. A built-in diff viewer, terminal, and PR tracking let you dispatch and review 10+ agents at once.
Packages reusable GitHub Copilot building blocks — agents, prompts, instructions, and skills — to make AI-assisted coding repeatable and standards-aligned for a team. Built around an RPI (Research, Plan, Implement) workflow in VS Code.
Provides an IntelliJ IDEA plugin that embeds both Anthropic Claude Code and OpenAI Codex into the IDE — conversation-aware code assistance, @file context, session management, agent/skills commands, and provider switching for in-IDE AI coding workflows.
Provides an in‑IDE interface for IntelliJ IDEA to interact with Anthropic Claude Code and OpenAI Codex for AI-assisted coding. Supports dual-engine switching, file-aware context, session history, agent skills, MCP extensions, and security/permission controls.
Coordinates about a dozen role-based AI agents — analyst, architect, developer, QA, scrum master — through a CLI, taking a feature from PRD and architecture docs into an automated dev cycle. Runs inside Claude Code, Cursor, Codex, or Gemini.
Provides a CLI-first framework to orchestrate autonomous AI agents and development workflows. Includes role-based agents, the ADE execution pipeline, IDE hooks and an NPX installer for quick setup—best for teams automating planning→development→QA.
Terminal-first developer workspace with an agentic AI side-panel that runs against your API keys or local models. Bundles a native PTY terminal, CodeMirror editor with AI edit diffs, file explorer, git history/graph, and a web preview in a ~7–8MB desktop app with no telemetry.
Parses local AI coding-assistant session logs and presents a privacy-first dashboard that surfaces practice scores, anti-patterns, code-output metrics, skill discovery, and context-health checks. Runs as a VS Code extension or a GitHub Copilot canvas; requires building/installing the VSIX.
Proposes SkillOpt-Lite, a minimal pipeline for optimizing LLM agent skills by treating rollout traces as filesystem files and applying trajectory exploration, consensus mining, and independent validation; integrates as a one-line VSCode Copilot command and reports cross-benchmark improvements that let smaller models sometimes outperform larger ones.