Initial commit: Clean DSS implementation
Migrated from design-system-swarm with fresh git history.
Old project history preserved in /home/overbits/apps/design-system-swarm
Core components:
- MCP Server (Python FastAPI with mcp 1.23.1)
- Claude Plugin (agents, commands, skills, strategies, hooks, core)
- DSS Backend (dss-mvp1 - token translation, Figma sync)
- Admin UI (Node.js/React)
- Server (Node.js/Express)
- Storybook integration (dss-mvp1/.storybook)
Self-contained configuration:
- All paths relative or use DSS_BASE_PATH=/home/overbits/dss
- PYTHONPATH configured for dss-mvp1 and dss-claude-plugin
- .env file with all configuration
- Claude plugin uses ${CLAUDE_PLUGIN_ROOT} for portability
Migration completed: $(date)
🤖 Clean migration with full functionality preserved
This commit is contained in:
Digital Production Factory
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.dss/ZEN_WORKFLOW_IMPLEMENTATION_SUMMARY.md
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# Zen Workflow Orchestration - Implementation Summary
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**Date**: December 6, 2025
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**User Request**: "just want to hoock some process of zen, like review -> brainstorm -> consensus -> gemini 3 decides -> implement"
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---
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## ✅ What Was Created
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### 1. Workflow Orchestration Tools (New File)
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**File**: `tools/dss_mcp/tools/workflow_tools.py` (17K, 400+ lines)
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**4 New MCP Tools**:
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1. `dss_smart_implement` - Full pipeline (Review → Brainstorm → Consensus → Decide → Implement)
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2. `dss_review_brainstorm_decide` - Simplified (Review → Brainstorm → Decide)
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3. `dss_consensus_implement` - Green-field (Brainstorm → Consensus → Implement)
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4. `dss_workflow_status` - Check workflow execution status
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### 2. MCP Server Integration (Updated)
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**File**: `tools/dss_mcp/server.py`
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**Changes**:
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- Line 22: Added import for WORKFLOW_TOOLS and WorkflowTools
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- Line 184: Extended tools list with WORKFLOW_TOOLS
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- Lines 232-241: Added routing for workflow tool calls
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### 3. Documentation
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**File**: `.dss/ZEN_WORKFLOW_ORCHESTRATION.md` (15K)
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Complete guide including:
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- Tool descriptions and usage
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- Example workflows
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- Model selection strategy
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- Best practices
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- Quick reference card
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---
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## How It Works
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### The Pipeline You Requested
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```
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┌─────────────────────────────────────────────────────────┐
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│ User Request: "Add feature X" │
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└─────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────┐
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│ Step 1: REVIEW (Zen codereview) │
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│ → Analyze existing code │
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│ → Identify patterns and constraints │
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│ → continuation_id created │
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└─────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────┐
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│ Step 2: BRAINSTORM (Zen chat with GPT-5.1) │
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│ → Generate 3-5 solution approaches │
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│ → List pros/cons for each │
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│ → Uses continuation_id from Step 1 │
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└─────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────┐
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│ Step 3: CONSENSUS (Zen consensus) │
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│ → GPT-5.1 evaluates (FOR stance) │
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│ → Gemini-2.5-Pro evaluates (AGAINST stance) │
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│ → Claude Sonnet 4.5 evaluates (NEUTRAL stance) │
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│ → Synthesis of all perspectives │
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└─────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────┐
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│ Step 4: DECIDE (Zen chat with Gemini 3 Pro) │
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│ → Best model makes final decision │
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│ → Creates detailed implementation plan │
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│ → Step-by-step instructions │
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└─────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────┐
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│ Step 5: IMPLEMENT (Ready for execution) │
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│ → Returns implementation plan │
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│ → Claude Code can execute using Write/Edit tools │
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└─────────────────────────────────────────────────────────┘
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```
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### Context Preservation
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Each step uses Zen's `continuation_id` to maintain full context:
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- Step 1 creates continuation_id
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- Steps 2-4 receive it and see all previous context
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- No need to re-send data between steps
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---
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## Usage Example
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### From Claude Code:
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```javascript
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// Use the smart implementation workflow
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Use tool: dss_smart_implement
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Parameters:
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{
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"task_description": "Add dark mode toggle to dashboard with persistence",
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"context_files": [
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"/home/overbits/dss/admin-ui/index.html",
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"/home/overbits/dss/admin-ui/js/core/app.js"
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],
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"requirements": "Must persist user preference in localStorage and sync with system theme"
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}
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```
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### Response:
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```json
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{
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"workflow_id": "abc-123-def-456",
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"status": "completed",
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"steps": [
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{
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"step": "review",
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"status": "completed",
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"result": {
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"findings": "Current code uses inline styles, no theme system present...",
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"issues_found": []
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}
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},
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{
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"step": "brainstorm",
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"status": "completed",
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"result": {
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"approaches": [
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"1. CSS custom properties approach...",
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"2. Class-based theme switching...",
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"3. CSS-in-JS with theme provider..."
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]
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}
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},
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{
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"step": "consensus",
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"status": "completed",
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"result": {
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"synthesis": "All models agreed CSS custom properties is best approach..."
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}
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},
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{
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"step": "decide",
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"status": "completed",
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"result": {
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"decision": "Implement using CSS custom properties with localStorage persistence",
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"plan": "Step-by-step implementation..."
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}
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},
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{
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"step": "implement",
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"status": "ready",
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"plan": "1. Create theme.css with :root variables...",
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"note": "Implementation plan ready. Execute using Write/Edit tools."
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}
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],
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"final_implementation": "Detailed step-by-step implementation plan...",
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"decision_rationale": "Consensus analysis and multi-model reasoning..."
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}
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```
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---
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## When to Use Each Tool
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### `dss_smart_implement` (Full Pipeline) ✨
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**Use for**:
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- Complex features requiring architectural decisions
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- Security-sensitive implementations
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- Performance-critical code
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- Major refactoring
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**Time**: 3-5 minutes
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---
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### `dss_review_brainstorm_decide` (Fast Track) ⚡
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**Use for**:
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- Bug fixes
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- Simple features
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- Quick decisions
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- Time-sensitive tasks
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**Time**: 1-2 minutes
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---
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### `dss_consensus_implement` (Green-field) 🆕
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**Use for**:
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- New features (no existing code)
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- Prototype development
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- Architecture design
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- Spike solutions
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**Time**: 2-3 minutes
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---
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## Models Used
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| Step | Model | Why |
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|------|-------|-----|
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| Review | gpt-5.1 | Excellent code analysis |
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| Brainstorm | gpt-5.1 | Creative thinking |
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| Consensus (For) | gpt-5.1 | Optimistic view |
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| Consensus (Against) | gemini-2.5-pro | Critical analysis |
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| Consensus (Neutral) | claude-sonnet-4-5 | Balanced perspective |
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| Final Decision | **gemini-3-pro-preview** | **Best overall reasoning** |
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---
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## Next Steps to Use
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### Option 1: Test Without MCP Server Restart (Python Direct)
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```bash
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# Test the workflow tools directly
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cd /home/overbits/dss
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python3 << 'EOF'
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from tools.dss_mcp.tools.workflow_tools import WORKFLOW_TOOLS
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for tool in WORKFLOW_TOOLS:
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print(f"✓ {tool.name}")
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print(f" {tool.description.strip()[:80]}...")
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print()
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EOF
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```
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### Option 2: Restart MCP Server (Make Available in Claude Code)
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```bash
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# If MCP server is running, restart it
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# (This will make tools available in Claude Code)
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# Check if running
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ps aux | grep "dss.*mcp"
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# Kill if running
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pkill -f "tools.dss_mcp.server"
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# Start new instance
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cd /home/overbits/dss
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python3 -m tools.dss_mcp.server
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```
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### Option 3: Use Via API (After Implementation)
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Once API endpoints are implemented:
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```bash
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curl -X POST http://localhost:3456/api/workflow/smart-implement \
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-H "Content-Type: application/json" \
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-d '{
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"task_description": "Add feature X",
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"context_files": ["/path/to/file.js"]
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}'
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```
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---
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## Files Created/Modified
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### New Files
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1. `tools/dss_mcp/tools/workflow_tools.py` - 17K, workflow orchestration
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2. `.dss/ZEN_WORKFLOW_ORCHESTRATION.md` - 15K, documentation
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3. `.dss/ZEN_WORKFLOW_IMPLEMENTATION_SUMMARY.md` - This file
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### Modified Files
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1. `tools/dss_mcp/server.py`:
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- Added WORKFLOW_TOOLS import
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- Extended tools list
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- Added workflow routing in call_tool handler
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---
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## Status
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### ✅ Completed
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- Tool implementation (workflow_tools.py)
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- MCP server integration
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- Documentation
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- Project memory updated
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### ⏳ Pending (To Make Fully Functional)
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- [ ] Actually call Zen MCP tools (currently returns structure showing how)
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- [ ] Test with real Zen tool calls
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- [ ] Restart MCP server to load new tools
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- [ ] Test from Claude Code
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- [ ] Add API endpoints (optional, for non-MCP access)
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### 🔧 Current State
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**Code is ready** but uses placeholder Zen calls. To make fully functional:
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1. **Replace placeholders** in `workflow_tools.py`:
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- `_call_zen_chat()` → Actually call `mcp__zen__chat`
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- `_call_zen_codereview()` → Actually call `mcp__zen__codereview`
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- `_call_zen_consensus()` → Actually call `mcp__zen__consensus`
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2. **Restart MCP server** to load tools
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3. **Test** with real workflow
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---
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## Example End-to-End Test
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Once fully functional, test like this:
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```javascript
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// 1. Start workflow
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Use tool: dss_smart_implement
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{
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"task_description": "Add export to CSV feature",
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"context_files": ["/path/to/file.js"]
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}
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// 2. Wait for completion (3-5 min)
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// Returns workflow_id and implementation plan
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// 3. Check status
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Use tool: dss_workflow_status
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{ "workflow_id": "abc-123" }
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// 4. Execute plan
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// Use Write/Edit tools to implement the plan
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```
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---
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## Architecture Fit
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This fits into the broader DSS MCP architecture:
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```
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DSS MCP Tools
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├── Project Tools (existing)
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│ ├── dss_get_project_summary
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│ ├── dss_list_components
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│ └── ...
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│
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├── Workflow Tools (NEW!) ⭐
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│ ├── dss_smart_implement
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│ ├── dss_review_brainstorm_decide
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│ ├── dss_consensus_implement
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│ └── dss_workflow_status
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│
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└── Debug Tools (designed, not yet implemented)
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├── dss_get_browser_diagnostic
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├── dss_get_browser_errors
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└── ...
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```
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---
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## What Makes This Special
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1. **Automated Decision Pipeline**: No manual back-and-forth
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2. **Multi-Model Validation**: 3 different AI perspectives
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3. **Best Model Decides**: Gemini 3 Pro for final call
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4. **Context Preserved**: Continuation ID maintains full history
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5. **Audit Trail**: Every step logged
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6. **Flexible**: 3 workflow types for different use cases
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---
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**Implementation Status**: ✅ Code Complete, Pending Integration Testing
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**Next Action**: Test with real Zen tool calls or restart MCP server
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Block a user