antigravity-workspace-template and hatch3r
One appears to be a starter kit providing a template for AI IDEs and agentic coding environments, while the other is a production-ready setup for AI coding agents with pre-configured components and integrations, suggesting they are **complements** where the former could provide the base environment for the latter's agent configuration.
About antigravity-workspace-template
study8677/antigravity-workspace-template
🪐 The ultimate starter kit for AI IDEs, Claude code,codex, and other agentic coding environments.
Deploys a dynamic multi-agent cluster where each code module gets its own Agent that autonomously generates knowledge docs during `ag-refresh`, with a Router that intelligently routes IDE queries via MCP to the responsible ModuleAgent—grounding context in actual source code rather than static docs. Supports Cursor, Claude Code, Windsurf, VS Code, and other agentic IDEs through portable `.antigravity/` folder architecture, using OpenAI Agent SDK and LiteLLM for multi-LLM compatibility. CLI injects templates zero-dependency, while the optional engine adds multi-agent Q&A, git history analysis, and optional semantic search via GitNexus integration.
About hatch3r
hatch3r/hatch3r
Production-ready spec driven development setup for AI coding agents in any repo with one command. Pre-configured with 11 agents, 22 skills, 18 rules, 25 commands & MCP integrations for Cursor, GitHub Copilot, Claude Code, Windsurf, Amp, Codex, Gemini CLI, Cline & OpenCode.
Maintains a canonical source-of-truth in `.agents/` that generates tool-native configs (`.mdc`, `CLAUDE.md`, `.windsurfrules`, etc.) for 14+ coding environments, enabling spec-driven workflows across Cursor, Copilot, Claude Code, and others. Supports multi-repo workspaces with cascading sync, auto-detecting platform (GitHub/Azure DevOps/GitLab) and project context to selectively install agents, skills, rules, and MCP servers—with headless CI support via `--yes` flag. Full project lifecycle from board initialization through dependency-aware task selection, multi-iteration review loops, and semver-driven releases.
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