Claude Methodology: A Specialized Agent Methodology for AI-Assisted Development

Claude Methodology is a complete AI-assisted development methodology. Its core lies in building a system composed of 7 specialized agents and 5 automated hooks through a specialized collaboration model, combined with a standardized workflow to achieve end-to-end automated development management from requirements to deployment. This methodology draws on best practices from traditional development teams, fully leveraging the deep capabilities of AI in specific domains, covering key links such as TDD, code review, and DevOps.

With the widespread application of Large Language Models (LLMs) in the software development field, how to effectively organize and coordinate the work of AI assistants has become a key challenge to improve development efficiency. The Claude Methodology project is a systematic solution proposed to address this challenge, aiming to elevate AI-assisted development to a new level through specialized division of labor and automated processes.

Claude Methodology defines 7 core agents, each with clear responsibilities and applicable models:

• Orchestrator (Coordinator): Uses the Opus model, acting as the command center to manage the entire process, connect various stages, and mobilize other agents.
• Architect: Uses the Opus model, responsible for system design, data schema, and API contract definition, decomposing requirements into atomic tasks.
• Backend Developer/Frontend Developer: Uses the Sonnet model, responsible for backend implementation (following TDD) and frontend implementation (zero business logic), respectively.
• Database Specialist: Handles data schema, migration scripts, and query optimization.
• Security Reviewer: Uses the Opus model to perform read-only security audits (OWASP Top10, sensitive information leakage, etc.).
• QA: Focuses on functional verification, boundary case identification, ensuring test coverage ≥80%.

Automated Hooks

The methodology designs 5 key hooks that automatically perform checks at development nodes:

• Pre-commit Guard: Runs the test suite before submission;
• Pre-push Guard: Prevents direct pushes to the main branch, enforcing the PR process;
• Post-PR Creation: Triggers reviews after PR creation;
• Session Start Context: Displays branch, commit status, etc., at the start of a session;
• Context Monitor: Monitors context usage, warning at 35% remaining and severe warning at 25% remaining.

Standardized Workflow

The process is divided into:

1. Brainstorming: The coordinator clarifies requirements and outputs a structured brief;
2. Architecture Design: The architect designs a technical solution based on the brief;
3. Implementation: Backend/frontend teams work in parallel using TDD (Red→Green→Refactor);
4. Review: Security reviewer and QA conduct reviews simultaneously, requiring both to pass;
5. Merge: Merge into the main branch after all checks pass.

Tech Stack Agnosticism

Agents automatically detect the tech stack (Node.js+TS/JS, Python+pytest, etc.) by reading the project's CLAUDE.md. The architect selects tools (Zod, Pydantic, etc.) based on the stack to adapt to various projects.

State Persistence

Planning information, task status, etc., are stored in the .planning/ directory to ensure complete context retention after session interruptions.

Enforced Development Norms

Enforced through hooks and review processes:

• Test coverage ≥80%;
• Must pass both security and quality reviews;
• Strict TDD process;
• Direct pushes to the main branch are prohibited;
• No stubs/TODO markers in code;
• Lightweight frontend with no business logic.

Claude Methodology provides a reference practical framework for AI-assisted development, proving that multi-AI agent collaboration can approach or exceed the efficiency of traditional teams. For teams hoping to introduce AI-assisted development, this methodology is a validated starting point—they can adjust agent configurations and processes according to their own needs, gradually establishing a system suitable for themselves, reducing transformation risks, and enabling AI technology to truly land and generate value.