Building the infrastructure for AI-powered software engineering.
构建 AI 驱动软件工程的基础设施。
FIT Lab - AI builds tools and systems that bring standard engineering practices to AI-assisted development — making AI coding agents work together with consistent workflows, shared skills, and auditable task lifecycles.
我们开发工具和系统，为 AI 辅助开发引入标准化工程实践——让 AI 编程 Agent 以统一的工作流、共享的技能体系和可审计的任务生命周期协同工作。
| Project | Description | Status |
|---|---|---|
| agent-infra | The collaboration layer for AI coding agents. Unified skills and workflows across Claude Code, Codex, Gemini CLI, and OpenCode — from issue to merged PR in 9 commands. | Active |
| AI 编程 Agent 的协作层。跨 Claude Code、Codex、Gemini CLI、OpenCode 的统一技能与工作流——从 Issue 到合并 PR 只需 9 条命令。 | ||
| More coming soon / 更多项目即将推出 |
Establish standalone value for each core project.
为每个核心项目建立独立价值。
- AI Agent Infrastructure — A complete skill-driven task lifecycle for AI coding agents: requirement analysis, technical design, implementation, code review, and delivery — working identically across multiple AI TUIs
AI Agent 基础设施——完整的技能驱动任务生命周期：需求分析、技术设计、代码实现、代码审查、交付，在多个 AI TUI 中行为一致 - Multi-Agent Collaboration — Foundation for agents to work as a team: identity, memory, messaging, and coordinated task execution
多 Agent 协作——Agent 团队协作基础：身份系统、共享记忆、消息通信、协调任务执行
Link the projects to unlock cross-cutting capabilities.
打通项目间连接，释放跨项目能力。
- Multi-agent system orchestrates infrastructure skills as shared execution primitives
多 Agent 系统将基础设施技能作为共享执行原语进行编排 - End-to-end: natural language requirement → multi-agent collaboration → deployed, reviewed code
端到端场景：自然语言需求 → 多 Agent 协作 → 已审查的代码部署
Grow beyond the core team.
超越核心团队，向社区扩展。
- Plugin architecture for third-party skills, workflows, and agent configurations
第三方技能、工作流和 Agent 配置的插件架构 - Community templates and best practices
社区模板和最佳实践 - Cross-organization, cross-team collaboration patterns
跨组织、跨团队协作模式