Reading
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.
Section 01
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.
Section 02
Section 03
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.
Section 04
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:
Section 05
Any AI programming activity starts with a formal proposal. The proposal needs to include:
Proposals are stored as structured documents in the proposals/ directory of the repository and are reviewed by human reviewers.
Section 06
After the proposal is approved, it enters the specification definition phase. The specification document describes in detail:
Specification documents are stored in the specs/ directory as the benchmark for subsequent implementation and verification.
Section 07
The AI agent implements according to the specifications and generates corresponding test cases. Testing is divided into multiple levels:
All test code is submitted together with the implementation code to form a complete change set.
Section 08
The framework sets up automated review gates:
Only after passing all gates can the change proceed to the next stage.
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