# RigorLoop: A Git-First Rigorous Workflow Framework for AI Programming Agents > A Git-first workflow framework designed for AI coding agents, covering proposals, specifications, testing, review gates, and persistent verification evidence to achieve a complete closed loop from idea to PR. - 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm) - 发布时间: 2026-05-25T11:15:27.000Z - 最近活动: 2026-05-25T11:22:29.488Z - 热度: 159.9 - 关键词: AI编程, Git工作流, 代码质量, 自动化测试, 代码审查, AI代理, 软件工程, 持续集成 - 页面链接: https://www.zingnex.cn/en/forum/thread/rigorloop-aigit - Canonical: https://www.zingnex.cn/forum/thread/rigorloop-aigit - Markdown 来源: floors_fallback --- ## Introduction / Main Post: RigorLoop: A Git-First Rigorous Workflow Framework for AI Programming Agents A Git-first workflow framework designed for AI coding agents, covering proposals, specifications, testing, review gates, and persistent verification evidence to achieve a complete closed loop from idea to PR. ## Original Author and Source - **Original Author/Maintainer**: xiongxianfei - **Source Platform**: GitHub - **Original Title**: rigorloop - **Original Link**: https://github.com/xiongxianfei/rigorloop - **Publication Date**: May 25, 2026 --- ## Background: Trust Crisis of AI Programming Agents With the popularity of AI programming assistants like GitHub Copilot, Cursor, and Claude Code, more and more code is generated by AI. However, the quality of AI-generated code varies, and there is a risk of "hallucination"—where AI may produce code that seems reasonable but is actually incorrect. This uncertainty brings new challenges to code review and project maintenance. Traditional software development processes assume code is written by humans, and reviewers can make judgments based on the author's ability and context. But when the code source becomes AI, this assumption no longer holds. We need new workflows to ensure the credibility, traceability, and verifiability of AI-generated code. The RigorLoop project was born to solve this problem. It proposes a complete Git-first workflow that integrates the activities of AI programming agents into a strict engineering management system. --- ## Project Overview: RigorLoop Framework RigorLoop is an open-source workflow framework for AI programming agents, created by developer xiongxianfei. The words "Rigor" (strictness) and "Loop" (closed loop) in the project name accurately summarize its core concept: ensuring the quality of AI-generated code through rigorous processes and forming a complete closed loop from requirements to delivery. The framework's core design principles include: 1. **Git-First**: All work products use the Git repository as the sole data source to ensure version control and traceability 2. **Structured Proposals**: Every work intention of the AI must be recorded as a structured proposal 3. **Specification-Driven**: Clear specifications must exist before implementation as the verification benchmark 4. **Automated Testing**: Every change must have corresponding test coverage 5. **Review Gates**: Multiple quality gates are set up; only passing all allows entry to the next stage 6. **Persistent Evidence**: All verification results are stored persistently to form an auditable evidence chain --- ## Phase 1: Proposal Any AI programming activity starts with a formal proposal. The proposal needs to include: - **Problem Statement**: Clearly state the problem to solve or the feature to implement - **Motivation**: Explain why this change is needed - **Solution Overview**: Describe the proposed implementation plan - **Impact Assessment**: Analyze the impact on the existing system - **Risk Identification**: List potential risks and mitigation measures Proposals are stored as structured documents in the `proposals/` directory of the repository and are reviewed by human reviewers. ## Phase 2: Specification After the proposal is approved, it enters the specification definition phase. The specification document describes in detail: - **Functional Specification**: Precise description of expected behavior - **Interface Contract**: API signatures, data formats, error handling specifications - **Performance Requirements**: Constraints such as response time and resource consumption - **Boundary Conditions**: Handling of abnormal scenarios and edge cases Specification documents are stored in the `specs/` directory as the benchmark for subsequent implementation and verification. ## Phase 3: Implementation and Testing The AI agent implements according to the specifications and generates corresponding test cases. Testing is divided into multiple levels: - **Unit Testing**: Verify the correctness of individual functions/modules - **Integration Testing**: Verify collaboration between modules - **Contract Testing**: Verify compliance with the interface contract defined in the specifications - **Regression Testing**: Ensure existing functions are not broken All test code is submitted together with the implementation code to form a complete change set. ## Phase 4: Review Gates The framework sets up automated review gates: - **Static Analysis**: Code style and potential bug scanning - **Test Pass**: All tests must pass - **Coverage Check**: Ensure the minimum coverage threshold is met - **Specification Compliance**: Verify that the implementation conforms to the specification document - **Security Scan**: Detect potential security vulnerabilities Only after passing all gates can the change proceed to the next stage.