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Miniature Guacamole: A Product Development Team System with 19 Collaborative Agents

A multi-agent product development system built on Claude Code, which implements a complete TDD/BDD development workflow through 19 specialized agents and 16 skills.

多智能体系统Claude CodeTDDBDD软件开发智能体协作自动化开发产品团队
Published 2026-04-07 01:44Recent activity 2026-04-07 01:52Estimated read 6 min
Miniature Guacamole: A Product Development Team System with 19 Collaborative Agents
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Section 01

Introduction: Core Overview of the Miniature Guacamole Multi-Agent Product Development System

Miniature Guacamole is a multi-agent product development system built on Claude Code. It simulates the collaborative process of a real product team through 19 specialized agents and 16 skill modules, enabling a fully automated TDD/BDD development workflow from requirements to delivery. This system represents a new paradigm of multi-agent collaboration in AI software engineering.

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Section 02

Background: Multi-Agent Collaboration Becomes a New Trend in AI Software Engineering

With the improvement of large language model capabilities, a single AI assistant can handle complex programming tasks, but it is difficult to balance multi-dimensional work such as requirement analysis and architecture design. This limitation has given rise to multi-agent collaboration systems—assigning professional tasks to specialized agents to simulate human team collaboration models. Miniature Guacamole is a typical representative of this trend.

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Section 03

Methodology: Role Division Design of 19 Agents

The system draws on agile team role division with clear agent responsibilities:

  • Product Management: Product Manager (requirements collection/prioritization), Business Analyst (requirements refinement)
  • Technical Architecture: System Architect (overall solution), Database Architect (data model), DevOps Engineer (CI/CD/containerization)
  • Development Implementation: Frontend/Backend/Full-stack Development Agents
  • Quality Assurance: Test Engineer (test strategy/test cases), QA Analyst (exploratory testing), Security Auditor (code security)
  • Support & Optimization: Technical Documentation (automatic document generation), Code Review (peer review), Performance Optimization (bottleneck identification)
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Section 04

Methodology: 16 Core Skill Modules Supporting Collaboration

The system defines 16 reusable skills:

  • Requirements Engineering: User story writing, acceptance criteria definition, requirement prioritization
  • Design & Architecture: Domain-driven design, API design, data modeling
  • Development & Implementation: TDD, BDD, code refactoring
  • Quality Assurance: Unit test writing, integration test design, performance test execution
  • Engineering Practices: Version control workflow, continuous integration configuration, containerized deployment, monitoring and observability
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Section 05

Practice: Automated Execution Process of TDD/BDD Workflow

The core value of the system is to automate the TDD/BDD process, with the following steps:

  1. Requirements Clarification: Product Manager + Business Analyst convert requirements into user stories and Gherkin-format acceptance criteria
  2. Test-First: Test Engineer writes failing test cases (Red phase)
  3. Feature Implementation: Development agents write minimal code to pass the tests (Green phase)
  4. Code Refactoring: Code Review + Performance Optimization agents guide refactoring while keeping tests passing
  5. Integration Verification: Multi-agent collaboration for integration testing, with parallel security audit scanning
  6. Documentation Delivery: Technical Documentation agent automatically generates synchronized documents
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Section 06

Conclusion & Recommendations: Applicable Scenarios and Limitations

Applicable Scenarios:

  • Standardized product development (following mature norms to reduce omissions)
  • Rapid prototype validation (accelerating market validation)
  • Legacy system maintenance (rebuilding knowledge to assist transformation)
  • 24/7 continuous delivery (not limited by time)

Limitations:

  • Highly innovative projects require the intuition of human architects
  • Agent coordination overhead and error propagation risks
  • Dependence on the Claude Code platform

These factors need to be weighed in practical applications.