🧠 DSH Expert Mode

August 27, 2026 · View on GitHub

1 Coordinator + 17 Experts — Full-Stack Multi-Agent Team
首席协调官 + 17 位领域专家 — 全栈多智能体团队

DSH Expert Mode main interface

✨ What it does

Install this preset and DSH automatically becomes a "Chief Coordinator" mode:

Scenario	Behavior
Receives task	Identifies domain → delegates to the best expert
Complex tasks	Dispatches multiple experts in parallel
Simple tasks	Coordinator handles directly — no forced delegation
Task complete	Experts stay online for follow-up modifications

No custom prompts to write. No multi-config to maintain. Just install and use.

🖼️ Demo

DSH Expert Mode main interface
Select the "Expert Mode" preset in DSH workspace to use

Expert Mode running
5 expert subagents working in parallel, with real-time token usage and timing

🧩 17 Experts

🎯 Full-Stack Core (6)

Expert	Tool	Domain
🖥️ Frontend Dev	`expert_frontend_dev`	Web frontend, React/Vue, CSS/UI
🖥️ Backend Dev	`expert_backend_dev`	API, server logic, authentication
🗄️ Database	`expert_database`	Schema design, SQL, optimization
🏗️ Architect	`expert_architect`	System design, tech selection
🛠️ DevOps	`expert_devops`	CI/CD, Docker, K8s, deployment
🧪 QA Engineer	`expert_qa_engineer`	Testing strategy, automation

🔒 Security & Data (3)

Expert	Tool	Domain
🔒 Security	`expert_security`	Code audit, vulnerabilities, hardening
📊 Data Analyst	`expert_data_analyst`	Statistics, visualization, insights
🎨 UI/UX Design	`expert_uiux_design`	Interface design, design systems

💼 Business (7)

Expert	Tool	Domain
📋 Product Manager	`expert_product_manager`	PRD, requirements, competitor research
✍️ Copywriter	`expert_copywriter`	Marketing copy, content creation
🎬 Media Creator	`expert_media_creator`	Storyboard, AI image, AI video, final cut
⚖️ Legal Review	`expert_legal_review`	Contract review, legal risk
📱 Social Media	`expert_social_media`	Multi-platform distribution
🚀 Growth Hacker	`expert_growth`	Growth strategy, A/B testing
💹 Quant Finance	`expert_quant_finance`	Quantitative models, risk
💰 Finance	`expert_finance`	Financial analysis, budget

🛡️ Features

Feature	Description
🎯 Smart Delegation	Auto-identifies task domain and routes to the best expert
🚀 Fast Track	Simple tasks handled directly — no forced delegation
🔄 Five-Anchor Constraint	Prevents topic drift with per-turn self-check
🤝 Cross Review	High-risk tasks get multi-expert independent review
💾 Experience Pool	Lessons learned are saved and injected next time
💬 Inter-Expert Bus	File-based message bus (bus.py): experts send/read directly, zero coordinator relay, P2P capable
📋 Taskboard	File-system task scheduler (taskboard.py): pending/ready/running/done/failed state machine, dependency DAG, retry, crash recovery — real scheduling, not just chat coordination
🚦 Quality Gates	5-stage pipeline for high-risk tasks: requirement clarity → implementation → verification → review → integration. Independent-expert review with 2-round rework limit
⚡ Fault Recovery	Auto-retry on timeout, strategy switch on failure
📉 Progressive Disclosure	Methodology injected on-demand, 28% token savings
🌐 Bilingual	Complete EN/ZH documentation

📦 Installation

Option A: npm one-click (recommended) 🚀

The package is published on npm as dsh-expert-mode. You can install it with the DSH plugin manager or npm directly:

# In DSH workspace — via plugin manager
dsh plugin add dsh-expert-mode

# ...or install the npm package directly
npm install dsh-expert-mode

ℹ️ How agent-presets work: this is an agent-preset plugin, not a Cordis service plugin. Installing the npm package pulls all files into your node_modules — but the preset only activates once its files are mounted into DSH's preset discovery directory. The preset ships a copy step (below) that makes this one command.

Option B: One-command preset mount (recommended for activation)

After installing the npm package, mount the preset into DSH's preset discovery directory:

# 1. Find where npm put the package
#    (usually ./node_modules/dsh-expert-mode in your DSH workspace, or globally)

# 2. Mount the preset into DSH's agent-presets directory
mkdir -p ~/.dsh/.agent-presets/expert-mode
cp -r node_modules/dsh-expert-mode/agent.cordis.yml \
      node_modules/dsh-expert-mode/preset.yml \
      node_modules/dsh-expert-mode/cordis.patch.yml \
      ~/.dsh/.agent-presets/expert-mode/
# If you want the full methodology docs (methods/, experts/, comm/ bus, taskboard):
# cp -r node_modules/dsh-expert-mode/.expert-mode ~/.dsh/.agent-presets/expert-mode/

# 3. Restart DSH web, then select "专家模式" in the workspace preset selector
dsh web

Note: ~/.dsh/.agent-presets/ is DSH's preset discovery directory. Each subdirectory = one preset. The preset name comes from preset.yml's name field.

Option C: Manual install from GitHub

Clone the repository, then copy the preset into DSH's agent-presets directory:

# 1. Clone anywhere
git clone https://github.com/Asher-2000/dsh-expert-mode.git
cd dsh-expert-mode

# 2. Copy the preset into DSH's agent-presets directory
mkdir -p ~/.dsh/.agent-presets/expert-mode
cp -r agent.cordis.yml preset.yml cordis.patch.yml ~/.dsh/.agent-presets/expert-mode/
# If you want the full methodology docs (methods/, experts/, comm/ bus), copy the whole tree:
# cp -r .expert-mode ~/.dsh/.agent-presets/expert-mode/

# 3. Restart DSH web, then select "专家模式" in the workspace preset selector
dsh web

Note: ~/.dsh/.agent-presets/ is DSH's preset discovery directory. Each subdirectory = one preset. The preset name comes from preset.yml's name field.

Then select "专家模式" in the workspace preset selector.

Optional: Cross-session memory (recommended)

The expert-mode preset itself does not register the cross-session memory service — it is a HOST-PLANE plugin, and registering it inside a preset conflicts with the host composition (causing preset mount failure). To enable cross-session memory, install dsh-memory-connect separately into the host composition:

# 1. Clone the memory plugin
git clone https://github.com/Asher-2000/dsh-memory-connect.git
cd dsh-memory-connect
npm install github:Asher-2000/dsh-memory-connect#v0.4.0  # or place it into the dsh dependency tree manually

# 2. Register it in the host composition (e.g. append to ~/.dsh/profiles/web/cordis.patch.yml):
# - id: cross-session-memory
#   name: '@deepseek-ai/dsh-memory-connect'
#   config:
#     path: ~/.dsh/memory.db
#     openAt: startup

# 3. Restart DSH web
dsh web

⚠️ Important: Do NOT add @deepseek-ai/dsh-memory-connect into this preset's agent.cordis.yml. It is a HOST-PLANE plugin (injects sessions + systemPrompt); registering it inside the preset throws service has been registered at <cross-session-memory>, which makes the expert-mode preset fail to mount and the UI fall back to the default preset. This preset ships with an explanatory comment about it.

🚀 Quick Start

Install the plugin
Select "专家模式" preset
Ask any question — the coordinator auto-delegates to the right expert

Example

User: 帮我设计一个用户认证系统

Coordinator:
  → 识别领域: 后端开发 + 安全
  → 委派 Backend Dev: API 设计、JWT 实现
  → 委派 Security: 安全审计、漏洞防护
  → 汇总输出完整方案

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Expert Mode Architecture                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │              Chief Coordinator (协调官)                    │   │
│  │  • Task analysis    • Domain identification               │   │
│  │  • Expert routing   • Result aggregation                  │   │
│  └──────────────────────────────────────────────────────────┘   │
│                           │                                      │
│           ┌───────────────┼───────────────┐                     │
│           ▼               ▼               ▼                     │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │  Frontend   │  │  Backend    │  │  DevOps     │            │
│  │  Database   │  │  Security   │  │  QA         │            │
│  │  Architect  │  │  ...        │  │  ...        │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

💬 Inter-Expert Communication Bus (v0.8.0)

┌──────────────────────────────────────────────────────────────┐
│                 File Message Bus (comm/bus.py)                │
│   .expert-mode/comm/mailboxes/<expert>/*.msg                 │
├──────────────────────────────────────────────────────────────┤
│                                                              │
│   data-analyst ──send──▶ frontend-dev   (direct, async)      │
│   copywriter   ──send──▶ social-media  (direct, async)       │
│   coordinator  ──broadcast──▶ all experts (global sync)      │
│   expert A     ──P2P subagent──▶ expert B  (synchronous)     │
│                                                              │
│   • Zero relay: content flows between experts, NOT through   │
│     coordinator context                                     │
│   • Durable: every message persisted as .msg file            │
│   • Auditable: full log at comm/logs/bus.log                 │
│   • Commands: send / read / ack / broadcast / stats          │
└──────────────────────────────────────────────────────────────┘

Communication Modes:

Mode	How	Use case
A. Relay	Expert A sends result → Expert B reads	Sequential collaboration
B. Parallel	Experts send results to coordinator → read --all	Independent collection
C. Broadcast	One message → all mailboxes	Global state changes
D. Review	Experts send "agree/partial/disagree + reason"	Cross review
E. P2P	Expert spawns subagent for direct Q&A	Synchronous clarification

📚 Documentation

Document	Description
Communication Protocol	Inter-expert message bus protocol v1
Expert Methods	16 expert methodology docs
Experience Pool	Lessons learned per expert
README.zh.md	中文文档

🤝 Contributing

Fork the repository
Create a feature branch
Commit your changes
Push to the branch
Open a Pull Request

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

DeepSeek Harness - The core framework
Cordis - Plugin system
Awesome DSH Plugin - Community listing