quickagents 2.1.1

QuickAgents - AI Agent Enhancement Toolkit

QuickAgents - AI Agent Project Initialization System

中文 | English

A complete AI agent project initialization system with out-of-the-box project setup, requirements clarification, multi-agent collaboration, and cross-session recovery capabilities.

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🚀 One-Line Installation

For Humans (Recommended)

Copy and paste this prompt into your AI agent (OpenCode CLI/Desktop, etc.):

Install and configure QuickAgents by following the instructions here:
https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/en/guide/installation.md

Or use pip for one-line installation:

pip install quickagents
qa --help

For LLM Agents

Fetch the installation guide and follow it:

curl -fsSL https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/en/guide/installation.md

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Core Features

🚀 Standardized Startup Process

Trigger Words (case-insensitive):

• 启动QuickAgent (Recommended)
• 启动QuickAgents
• 启动QA
• Start QA

💡 Tip: 启动QuickAgent is recommended for clarity and brevity.

🧠 Three-Dimensional Memory System

Based on the paper "Memory in the Age of AI Agents":

Memory Type	Purpose	Examples
Factual Memory	Static facts	Project metadata, technical decisions, business rules
Experiential Memory	Dynamic experiences	Operation history, lessons learned, user feedback
Working Memory	Active state	Current task, active context, pending decisions

🤖 17 Professional Agents

Category	Agents
Core	yinglong-init, boyi-consult, chisongzi-advise, cangjie-doc, huodi-skill, fenghou-orchestrate
Quality	jianming-review, lishou-test, mengzhang-security, hengge-perf
Tools	kuafu-debug, gonggu-refactor, huodi-deps, hengge-cicd
Planning	fenghou-plan, boyi-consult, jianming-review

📦 19 Specialized Skills

Core Skills (12)

Skill	Purpose
project-memory-skill	Three-dimensional memory management
boulder-tracking-skill	Cross-session progress tracking
category-system-skill	Semantic task classification
inquiry-skill	7-layer requirements inquiry
tdd-workflow-skill	Test-driven development workflow
code-review-skill	Code quality review
git-commit-skill	Git commit standardization
multi-model-skill	Multi-model support
lsp-ast-skill	LSP/AST code analysis
project-detector-skill	Project type detection
background-agents-skill	Parallel agent execution
skill-integration-skill	Skill integration management

Research-Based Skills (6) - v2.1.0

Skill	Purpose	Source
lazy-discovery-skill	Lazy tool loading, 50%+ context reduction	OpenDev
event-reminder-skill	Event-driven reminders, anti-instruction-fade	OpenDev
doom-loop-skill	Loop detection, prevent repeated calls	OpenDev
adaptive-compression-skill	Adaptive compression, 54% peak reduction	VeRO
vero-evaluation-skill	Versioned evaluation (V-E-R-O)	VeRO
aci-design-skill	ACI design principles	SWE-agent

Feedback Skills (1) - v2.1.1

Skill	Purpose
feedback-collector-skill	Collect usage experience for system improvement

📚 Complete Documentation System

• Hybrid Structure: Project-level + features/ + modules/
• Bidirectional Sync: Docs/ ↔ .opencode/memory/
• Knowledge Graph: INDEX.md provides complete document navigation

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Quick Start

1. Install QuickAgents

# Option 1: Use CLI
pip install quickagents
qa init

# Option 2: Use one-line prompt (recommended)
# Paste into your AI agent:
# Install and configure QuickAgents by following the instructions here:
# https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/en/guide/installation.md

2. Start Initialization

In OpenCode or compatible AI coding agent, send:

启动QuickAgent

3. First-Time Configuration

On first use, the system will automatically guide you through:

• models.json: AI model configuration
• lsp-config.json: LSP server configuration

4. Answer Inquiry Cards

AI will collect requirements through interactive inquiry cards using the 7-layer model:

1. L1 Business Essence: Why? Core pain points?
2. L2 User Profile: Who uses? Use cases?
3. L3 Core Flow: Complete process? Exception handling?
4. L4 Feature List: What to build? Feature boundaries?
5. L5 Data Model: Data structures? Relationships?
6. L6 Tech Stack: Frameworks? Database? Deployment?
7. L7 Delivery Standards: Acceptance criteria? Timeline?

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Agent Usage Guide

By Scenario

Scenario	Recommended Approach	Description
First Use	启动QuickAgent	Auto-invokes yinglong-init
Daily Development	Direct conversation	AI intelligently dispatches fenghou-orchestrate
Making Plans	@boyi-consult or @fenghou-plan	Enter Plan Mode
Actual Development	@fenghou-orchestrate or /ultrawork	Efficient execution

Core Commands

Command	Description
/start-work	Cross-session recovery
/ultrawork <task>	Ultra-efficient task execution
/run-workflow	Run workflow
/enable-coordination	Enable agent coordination
/disable-coordination	Disable agent coordination

📖 Detailed Guide: See Docs/en/AGENT_GUIDE.md for complete agent usage and collaboration diagrams.

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Project Structure

your-project/
├── AGENTS.md              # Development specification
├── Docs/                  # Project documentation
│   ├── MEMORY.md          # Three-dimensional memory
│   ├── TASKS.md           # Task management
│   ├── DESIGN.md          # Design documents
│   ├── INDEX.md           # Knowledge graph
│   ├── AGENT_GUIDE.md     # Agent usage guide
│   ├── USER_GUIDE.md      # User guide
│   ├── ARCHITECTURE.md    # Architecture docs
│   ├── en/                # English documentation
│   └── guide/             # Installation guides
│       └── installation.md
│
├── .opencode/             # OpenCode configuration
│   ├── agents/            # 17 professional agents
│   ├── skills/            # 19 specialized skills
│   ├── commands/          # 6 core commands
│   ├── hooks/             # Automation hooks
│   ├── config/            # Configuration files
│   │   ├── quickagents.json
│   │   ├── categories.json
│   │   └── lsp-config.json
│   └── memory/            # Project memory
│
└── .quickagents/          # QuickAgents data
    └── boulder.json       # Progress tracking

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Documentation

Document	Description	Language
README.md	Project overview	EN/CN
AGENT_GUIDE.md	Agent usage guide	EN
USER_GUIDE.md	User guide	EN
ARCHITECTURE.md	System architecture	EN
Installation Guide	Installation instructions	EN
中文文档	中文文档索引	CN

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Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Acknowledgments

• Inspired by oh-my-openagent
• Built for OpenCode

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Version: 2.1.1 | Last Updated: 2026-03-27

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QuickAgents - AI 代理项目初始化系统

中文 | English

一套完整的AI代理项目初始化系统，提供开箱即用的项目初始化、需求澄清、多代理协作和跨会话恢复能力

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🚀 一键安装

For Humans（推荐）

复制以下提示词，粘贴到你的AI代理（OpenCode CLI/Desktop等）：

Install and configure QuickAgents by following the instructions here:
https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/guide/installation.md

或者使用CLI一键安装：

pip install quickagents
qa init

For LLM Agents

获取安装指南并执行：

curl -fsSL https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/guide/installation.md

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核心特性

🚀 标准化启动流程

触发词（大小写不敏感）：

• 「启动QuickAgent」（推荐）
• 「启动QuickAgents」
• 「启动QA」
• 「Start QA」

💡 提示：推荐使用「启动QuickAgent」，简洁且语义明确

🧠 三维记忆系统

基于论文《Memory in the Age of AI Agents》设计：

记忆类型	用途	示例
Factual Memory	事实记忆	项目元信息、技术决策、业务规则
Experiential Memory	经验记忆	操作历史、经验总结、用户反馈
Working Memory	工作记忆	当前状态、活跃上下文、待决策项

🤖 17个专业代理

分类	代理
核心	yinglong-init, boyi-consult, chisongzi-advise, cangjie-doc, huodi-skill, fenghou-orchestrate
质量	jianming-review, lishou-test, mengzhang-security, hengge-perf
工具	kuafu-debug, gonggu-refactor, huodi-deps, hengge-cicd
规划	fenghou-plan, boyi-consult, jianming-review

📦 19个专项技能

核心技能 (12个)

技能	用途
project-memory-skill	三维记忆管理
boulder-tracking-skill	跨会话进度追踪
category-system-skill	语义化任务分类
inquiry-skill	7层需求问询
tdd-workflow-skill	测试驱动开发工作流
code-review-skill	代码质量审查
git-commit-skill	Git提交标准化
multi-model-skill	多模型支持
lsp-ast-skill	LSP/AST代码分析
project-detector-skill	项目类型检测
background-agents-skill	并行代理执行
skill-integration-skill	Skill整合管理

研究论文技能 (6个) - v2.1.0新增

技能	用途	来源
lazy-discovery-skill	工具懒加载，减少上下文50%+	OpenDev
event-reminder-skill	事件驱动提醒，对抗指令遗忘	OpenDev
doom-loop-skill	循环检测，防止重复调用	OpenDev
adaptive-compression-skill	自适应压缩，峰值减少54%	VeRO
vero-evaluation-skill	版本化评估(V-E-R-O)	VeRO
aci-design-skill	ACI设计原则	SWE-agent

经验收集技能 (1个) - v2.1.1新增

技能	用途
feedback-collector-skill	收集使用经验，助力系统进化

📚 完整文档体系

• 混合结构：项目级 + features/ + modules/
• 双向同步：Docs/ ↔ .opencode/memory/
• 知识图谱：INDEX.md 提供完整的文档导航

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快速开始

1. 安装QuickAgents

# 方式1：使用CLI
pip install quickagents
qa init

# 方式2：使用一句话提示词（推荐）
# 粘贴到AI代理中：
# Install and configure QuickAgents by following the instructions here:
# https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/guide/installation.md

2. 启动初始化

在 OpenCode 或兼容的 AI 编码代理中，发送：

启动QuickAgent

3. 首次配置引导

首次使用时，系统会自动引导你完成：

• models.json：AI模型配置
• lsp-config.json：LSP服务器配置

4. 回答询问卡

AI 会通过互动询问卡收集需求，按照7层扩展模型逐层澄清：

1. L1 业务本质：为什么做？核心痛点？
2. L2 用户画像：谁使用？使用场景？
3. L3 核心流程：完整流程？异常处理？
4. L4 功能清单：做什么？功能边界？
5. L5 数据模型：数据结构？关系？
6. L6 技术栈：框架？数据库？部署？
7. L7 交付标准：验收标准？时间节点？

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Agent使用指南

按场景使用代理

场景	推荐方式	说明
首次使用	启动QuickAgent	自动调用 yinglong-init
日常开发	直接对话	AI智能调度 fenghou-orchestrate
制定计划	@boyi-consult 或 @fenghou-plan	进入Plan Mode
实际开发	@fenghou-orchestrate 或 /ultrawork	高效执行

核心命令

命令	说明
/start-work	跨会话恢复
/ultrawork <任务>	超高效执行任务
/run-workflow	运行工作流
/enable-coordination	启用代理协调
/disable-coordination	禁用代理协调

📖 详细指南：查看 Docs/AGENT_GUIDE.md 了解完整的Agent调用和合作关系图

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项目结构

your-project/
├── AGENTS.md              # 开发规范
├── Docs/                  # 项目文档
│   ├── MEMORY.md          # 三维记忆
│   ├── TASKS.md           # 任务管理
│   ├── DESIGN.md          # 设计文档
│   ├── INDEX.md           # 知识图谱
│   ├── AGENT_GUIDE.md     # Agent使用指南
│   ├── USER_GUIDE.md      # 用户指南
│   ├── ARCHITECTURE.md    # 架构文档
│   ├── en/                # 英文文档
│   └── guide/             # 安装指南
│       └── installation.md
│
├── .opencode/             # OpenCode配置
│   ├── agents/            # 17个专业代理
│   ├── skills/            # 19个专项技能
│   ├── commands/          # 6个核心命令
│   ├── hooks/             # 自动化钩子
│   ├── config/            # 配置文件
│   │   ├── quickagents.json
│   │   ├── categories.json
│   │   └── lsp-config.json
│   └── memory/            # 项目记忆
│
└── .quickagents/          # QuickAgents数据
    └── boulder.json       # 进度追踪

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文档导航

文档	描述	语言
README.md	项目概览	CN/EN
AGENT_GUIDE.md	Agent使用指南	CN
USER_GUIDE.md	用户指南	CN
ARCHITECTURE.md	系统架构	CN
安装指南	安装说明	CN
English Docs	English documentation	EN

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贡献指南

欢迎贡献！提交PR前请阅读贡献指南。

1. Fork 仓库
2. 创建功能分支 (git checkout -b feature/amazing-feature)
3. 提交更改 (git commit -m 'feat: add amazing feature')
4. 推送到分支 (git push origin feature/amazing-feature)
5. 创建 Pull Request

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许可证

本项目采用 MIT 许可证 - 详情请查看 LICENSE 文件。

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致谢

• 灵感来源于 oh-my-openagent
• 为 OpenCode 构建

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版本: 2.1.1 | 更新时间: 2026-03-27