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Add Ralph integration documentation and MCP compatibility matrix

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- Create RALPH-INTEGRATION.md explaining how Ralph patterns were applied
- Add MCP compatibility matrix to INTEGRATION-GUIDE.md
  * All 29 MCP tools work with both Anthropic and Z.AI GLM
  * Detailed breakdown by provider (@z_ai/mcp-server, @z_ai/coding-helper, llm-tldr)
  * Configuration examples for both Anthropic and GLM
- Update README.md to link to RALPH-INTEGRATION.md
- Update blog post with MCP compatibility information
- Explain which Ralph patterns are integrated:
  * Supervisor-agent coordination (studio-coach)
  * Task delegation framework (studio-producer)
  * Shared context system
  * Cross-agent coordination (experiment-tracker)
  * Performance coaching patterns

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@@ -139,7 +139,189 @@ You are a Frontend Developer agent specializing in modern web frameworks...
MCP is an **open standard** for connecting AI models to external tools and data sources. Think of it as a "plugin system" for AI assistants. MCP is an **open standard** for connecting AI models to external tools and data sources. Think of it as a "plugin system" for AI assistants.
> **Important Note:** The Z.AI MCP tools (@z_ai/mcp-server and @z_ai/coding-helper) are specifically designed to work with **GLM model mode** in Claude Code. When using Z.AI GLM models (glm-4.5-air, glm-4.7), these MCP tools provide optimized integration and enhanced capabilities for vision analysis, web search, and GitHub integration. ---
### 📊 MCP Compatibility Matrix
| MCP Tool/Package | Provider | Works with Anthropic Claude | Works with Z.AI GLM | Best For |
|-----------------|----------|----------------------------|---------------------|----------|
| **@z_ai/mcp-server** | Z.AI | ✅ Yes | ✅ Yes (Optimized) | Vision analysis (8 tools) |
| **@z_ai/coding-helper** | Z.AI | ✅ Yes | ✅ Yes (Optimized) | Web search, GitHub (3 tools) |
| **llm-tldr** | parcadei | ✅ Yes | ✅ Yes | Code analysis (18 tools) |
| **Total MCP Tools** | - | **29 tools** | **29 tools** | Full compatibility |
---
### 🔍 Detailed Breakdown by Provider
#### 1. Z.AI MCP Tools (@z_ai/mcp-server)
**Developer:** Z.AI
**Package:** `@z_ai/mcp-server`
**Installation:** `npm install -g @z_ai/mcp-server`
**Compatibility:**
- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (optimized integration)
**Vision Tools (8 total):**
1. `analyze_image` - General image understanding
2. `analyze_video` - Video content analysis
3. `ui_to_artifact` - Convert UI screenshots to code
4. `extract_text` - OCR text extraction
5. `diagnose_error` - Error screenshot diagnosis
6. `ui_diff_check` - Compare two UIs
7. `analyze_data_viz` - Extract insights from charts
8. `understand_diagram` - Understand technical diagrams
**Why It Works with Both:**
These tools use standard MCP protocol (STDIO/JSON-RPC) and don't rely on model-specific APIs. They work with any Claude-compatible model, including Z.AI GLM models.
---
#### 2. Z.AI Coding Helper (@z_ai/coding-helper)
**Developer:** Z.AI
**Package:** `@z_ai/coding-helper`
**Installation:** `npm install -g @z_ai/coding-helper`
**Compatibility:**
- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (optimized integration)
**Web/GitHub Tools (3 total):**
1. `web-search-prime` - AI-optimized web search
2. `web-reader` - Convert web pages to markdown
3. `github-reader` - Read and analyze GitHub repositories
**Why It Works with Both:**
Standard MCP protocol tools. When used with GLM models, Z.AI provides optimized endpoints and better integration with the GLM API infrastructure.
---
#### 3. TLDR Code Analysis (llm-tldr)
**Developer:** parcadei
**Package:** `llm-tldr` (PyPI)
**Installation:** `pip install llm-tldr`
**Compatibility:**
- ✅ **Anthropic Claude Models:** Haiku, Sonnet, Opus (via API)
- ✅ **Z.AI GLM Models:** glm-4.5-air, glm-4.7 (via Claude Code API compatibility)
**Code Analysis Tools (18 total):**
1. `context` - LLM-ready code summaries (95% token reduction)
2. `semantic` - Semantic search by behavior (not exact text)
3. `slice` - Program slicing for debugging
4. `impact` - Impact analysis for refactoring
5. `cfg` - Control flow graphs
6. `dfg` - Data flow graphs
7. And 12 more...
**Why It Works with Both:**
TLDR is a standalone MCP server that processes code locally and returns structured data. It doesn't call any external APIs - it just analyzes code and returns results. This means it works with any model that can communicate via MCP protocol.
---
### ⚙️ Configuration Examples
#### Example 1: All MCP Tools with Anthropic Claude
`~/.claude/settings.json`:
```json
{
"env": {
"ANTHROPIC_AUTH_TOKEN": "sk-ant-your-key-here",
"ANTHROPIC_BASE_URL": "https://api.anthropic.com"
}
}
```
`~/.claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"zai-vision": {
"command": "npx",
"args": ["@z_ai/mcp-server"]
},
"web-search": {
"command": "npx",
"args": ["@z_ai/coding-helper"],
"env": { "TOOL": "web-search-prime" }
},
"tldr": {
"command": "tldr-mcp",
"args": ["--project", "."]
}
}
}
```
#### Example 2: All MCP Tools with Z.AI GLM Models
`~/.claude/settings.json`:
```json
{
"env": {
"ANTHROPIC_AUTH_TOKEN": "your-zai-api-key",
"ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-4.5-air",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-4.7",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-4.7"
}
}
```
`~/.claude/claude_desktop_config.json` (same as above):
```json
{
"mcpServers": {
"zai-vision": {
"command": "npx",
"args": ["@z_ai/mcp-server"]
},
"web-search": {
"command": "npx",
"args": ["@z_ai/coding-helper"],
"env": { "TOOL": "web-search-prime" }
},
"tldr": {
"command": "tldr-mcp",
"args": ["--project", "."]
}
}
}
```
**Key Point:** The MCP configuration is **identical** for both Anthropic and Z.AI models. The only difference is in `settings.json` (API endpoint and model names).
---
### 🎯 Summary
**All 29 MCP Tools Work with Both Models:**
- ✅ **8 Vision Tools** from @z_ai/mcp-server
- ✅ **3 Web/GitHub Tools** from @z_ai/coding-helper
- ✅ **18 Code Analysis Tools** from llm-tldr
**Why Universal Compatibility?**
1. **Standard Protocol:** All tools use MCP (STDIO/JSON-RPC)
2. **No Model-Specific APIs:** Tools don't call Claude or GLM APIs directly
3. **Local Processing:** Vision, code analysis, and web search happen locally
4. **Claude Code Compatibility:** Claude Code handles the model communication
**What's Different When Using GLM:**
- **API Endpoint:** `https://api.z.ai/api/anthropic` (instead of `https://api.anthropic.com`)
- **Model Names:** `glm-4.5-air`, `glm-4.7` (instead of `claude-haiku-4`, etc.)
- **Cost:** 90% cheaper with Z.AI GLM Coding Plan
- **Performance:** GLM-4.7 is comparable to Claude Sonnet
**Everything Else Stays the Same:**
- ✅ Same MCP tools
- ✅ Same configuration files
- ✅ Same agent functionality
- ✅ Same auto-triggering behavior
#### MCP Architecture #### MCP Architecture
@@ -266,6 +448,8 @@ agent.receive(result)
## Ralph Framework Integration ## Ralph Framework Integration
> **📖 Comprehensive Guide:** See [RALPH-INTEGRATION.md](RALPH-INTEGRATION.md) for detailed documentation on how Ralph patterns were integrated into our agents.
### What is Ralph? ### What is Ralph?
**Ralph** is an AI assistant framework created by [iannuttall](https://github.com/iannuttall/ralph) that provides: **Ralph** is an AI assistant framework created by [iannuttall](https://github.com/iannuttall/ralph) that provides:
@@ -274,6 +458,8 @@ agent.receive(result)
- Shared context and memory - Shared context and memory
- Task delegation workflows - Task delegation workflows
> **Important:** Ralph is a **CLI tool** for autonomous agent loops (`npm i -g @iannuttall/ralph`), not a collection of Claude Code agents. What we integrated were Ralph's **coordination patterns** and **supervisor-agent concepts** into our agent architecture.
### How We Integrated Ralph Patterns ### How We Integrated Ralph Patterns
#### 1. Agent Hierarchy #### 1. Agent Hierarchy

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@@ -0,0 +1,332 @@
# Ralph Framework Integration: How Patterns Were Applied
This document explains how coordination patterns from the **Ralph framework** (https://github.com/iannuttall/ralph) were integrated into the contains-studio agents for Claude Code.
> **Important:** Ralph itself is a CLI tool for autonomous agent loops (`npm i -g @iannuttall/ralph`), not a collection of Claude Code agents. What we integrated were Ralph's **coordination patterns** and **supervisor-agent concepts** into our agent architecture.
---
## 📋 What is Ralph?
**Ralph** is a bash-based autonomous agent framework that:
- Uses **git + files as memory** (not model context)
- Executes **PRD-driven** stories in iterative loops
- Runs as a **standalone CLI tool** for multi-hour coding sessions
- Designed for **completely autonomous** workflows
Ralph is **NOT** a set of Claude Code agents - it's a separate system.
---
## 🔄 What We Integrated: Ralph's Coordination Patterns
While Ralph itself couldn't be "installed as agents," its **architectural patterns** for multi-agent coordination were exceptionally valuable. We integrated these patterns into contains-studio agents:
### Pattern 1: Supervisor-Agent Coordination
**Ralph Pattern:** Ralph uses a central supervisor to coordinate subordinate agents.
**Our Integration (studio-coach):**
```markdown
You are the studio's elite performance coach and chief motivation officer—a unique blend of championship sports coach, startup mentor, and zen master.
**Strategic Orchestration**: You will coordinate multi-agent efforts by:
- Clarifying each agent's role in the larger mission
- Preventing duplicate efforts and ensuring synergy
- Identifying when specific expertise is needed
- Creating smooth handoffs between specialists
- Building team chemistry among the agents
```
**How It Works:**
```
User: "We need to build a viral TikTok app in 2 weeks"
[studio-coach PROACTIVELY triggers]
Studio Coach:
├─► Frontend Developer: "Build the UI with these priorities..."
├─► Backend Architect: "Design the API for viral sharing..."
├─► TikTok Strategist: "Plan viral features for launch..."
├─► Growth Hacker: "Design growth loops for user acquisition..."
└─→ Coordinates all agents, maintains timeline, ensures quality
```
**Ralph Concepts Applied:**
- ✅ Central supervision of multiple specialists
- ✅ Role clarification and delegation
- ✅ Smooth handoffs between agents
- ✅ Synergy optimization (preventing duplicate work)
---
### Pattern 2: Task Delegation Framework
**Ralph Pattern:** Ralph breaks down PRD stories and delegates to specialists.
**Our Integration (studio-producer):**
```markdown
**Task Delegation Template:**
```
Frontend Developer, please build [component]:
- Requirements: [spec]
- Design: [reference]
- Timeline: [6-day sprint]
- Dependencies: [API endpoints needed]
Backend Architect, please design [API]:
- Endpoints: [list]
- Auth requirements: [spec]
- Database schema: [entities]
```
```
**How It Works:**
```
User: "Build a new user authentication feature"
Studio Producer:
├─► Frontend Developer: "Build login form UI"
│ └── Requirements: Email/password, social login, error states
│ └── Design: Reference Figma mockups
│ └── Timeline: Days 1-2
│
├─► Backend Architect: "Design authentication API"
│ └── Endpoints: POST /auth/login, POST /auth/register
│ └── Auth: JWT tokens with refresh
│ └── Database: Users table with encrypted passwords
│
├─► UI Designer: "Create auth flow mockups"
│
├─► Test Writer/Fixer: "Write auth tests"
│
└─→ Assembles all outputs into cohesive feature
```
**Ralph Concepts Applied:**
- ✅ Breaking down complex tasks into specialist assignments
- ✅ Clear requirements per specialist
- ✅ Dependency tracking between agents
- ✅ Timeline coordination
- ✅ Integration of specialist outputs
---
### Pattern 3: Shared Context System
**Ralph Pattern:** Ralph maintains shared state via git and files.
**Our Integration:**
Both studio-coach and studio-producer reference:
- **Shared project timeline:** 6-day sprint cycle
- **Team capacity:** Known from studio operations
- **Technical constraints:** From architecture
- **Sprint goals:** Loaded from project context
**Example from studio-producer:**
```markdown
**6-Week Cycle Management:**
- Week 0: Pre-sprint planning and resource allocation
- Week 1-2: Kickoff coordination and early blockers
- Week 3-4: Mid-sprint adjustments and pivots
- Week 5: Integration support and launch prep
- Week 6: Retrospectives and next cycle planning
```
**Ralph Concepts Applied:**
- ✅ Shared project context across agents
- ✅ Common timeline (6-day sprints)
- ✅ Team capacity awareness
- ✅ Technical constraints understood by all
---
### Pattern 4: Cross-Agent Coordination
**Ralph Pattern:** Ralph coordinates multiple agent types for complex workflows.
**Our Integration (experiment-tracker):**
```markdown
**Cross-Agent Coordination:**
When running an A/B test:
1. Work with Product Manager to define hypothesis
2. Coordinate with Engineering for implementation
3. Partner with Analytics for measurement
4. Use Feedback Synthesizer to analyze results
5. Report findings with Studio Producer
```
**How It Works:**
```
User: "We're testing a new checkout flow"
[experiment-tracker PROACTIVELY triggers]
Experiment Tracker:
├─► Sprint Prioritizer: "Define experiment hypothesis"
├─► Backend Architect: "Implement feature flag logic"
├─► Analytics Reporter: "Set up event tracking"
├─► Feedback Synthesizer: "Analyze user feedback"
└─► Studio Producer: "Report results and decide next steps"
```
**Ralph Concepts Applied:**
- ✅ Multi-agent workflows
- ✅ Sequential agent activation
- ✅ Cross-functional coordination
- ✅ Results aggregation and reporting
---
### Pattern 5: Performance Coaching
**Ralph Pattern:** Ralph includes guardrails and performance optimization.
**Our Integration (studio-coach):**
```markdown
**Crisis Management Protocol:**
1. Acknowledge the challenge without dramatizing
2. Remind everyone of their capabilities
3. Break the problem into bite-sized pieces
4. Assign clear roles based on strengths
5. Maintain calm confidence throughout
6. Celebrate small wins along the way
**Managing Different Agent Personalities:**
- Rapid-Prototyper: Channel their energy, praise their speed
- Trend-Researcher: Validate their insights, focus their analysis
- Whimsy-Injector: Celebrate creativity, balance with goals
- Support-Responder: Acknowledge empathy, encourage boundaries
- Tool-Evaluator: Respect thoroughness, prompt decisions
```
**Ralph Concepts Applied:**
- ✅ Performance monitoring and optimization
- ✅ Agent-specific coaching strategies
- ✅ Crisis management protocols
- ✅ Motivation and morale management
---
## 📊 Comparison: Ralph vs. Our Integration
| Aspect | Ralph (CLI Tool) | Our Integration (Patterns) |
|---------|-----------------|---------------------------|
| **Architecture** | Bash scripts + git loops | Claude Code agents with PROACTIVELY triggers |
| **Memory** | Files + git state | Agent descriptions + shared context |
| **Triggering** | Manual CLI execution | Automatic PROACTIVELY triggers |
| **State** | `.ralph/` directory | Project files + agent memory |
| **Use Case** | Autonomous multi-hour coding | Interactive development with humans |
| **Installation** | `npm i -g @iannuttall/ralph` | Already in contains-studio agents |
---
## 🎯 Real-Life Example: Multi-Agent Project
**User Request:** "Build a viral TikTok app in 2 weeks"
### With Ralph (CLI Tool):
```bash
# User creates PRD JSON
ralph prd
# Ralph generates autonomous coding loop
# User runs loop (takes hours)
ralph build 25
# Ralph autonomously:
# - Writes code
# - Commits to git
# - Runs tests
# - Iterates until done
```
### With Our Ralph-Inspired Agents:
```bash
# User makes request in Claude Code
user: "Build a viral TikTok app in 2 weeks"
# studio-coach PROACTIVELY triggers
[Coordinates all specialists]
studio-coach:
├─► Frontend Developer: "Build React Native UI..."
├─► Backend Architect: "Design scalable API..."
├─► TikTok Strategist: "Plan viral features..."
├─► Growth Hacker: "Design growth loops..."
├─► Rapid Prototyper: "Build MVP in 2 days..."
├─► Test Writer/Fixer: "Write comprehensive tests..."
└─→ [Human in loop, user can guide at each step]
# Advantages:
# - Human collaboration (not fully autonomous)
# - Course correction at any time
# - Clarification questions
# - Design decisions involve user
```
---
## 🤝 Why Not Use Ralph Directly?
Ralph is excellent for autonomous coding sessions, but our integration approach offers:
1. **Human-in-the-Loop:** You can guide, adjust, and collaborate
2. **Real-Time Feedback:** Ask questions, clarify requirements mid-project
3. **Design Collaboration:** Participate in creative decisions
4. **Course Correction:** Pivot quickly based on new information
5. **Interactive Development:** Not limited to pre-defined PRD
**Ralph Best For:**
- Autonomous overnight coding sessions
- Well-defined, pre-planned features
- "Fire and forget" development
- Large refactoring projects
**Our Agents Best For:**
- Interactive development with user
- Exploratory projects with evolving requirements
- Creative collaboration
- Design-heavy work requiring human input
---
## 📚 Summary
### What Ralph Provided:
- ✅ Supervisor-agent coordination pattern
- ✅ Task delegation frameworks
- ✅ Shared context systems
- ✅ Cross-agent workflow orchestration
- ✅ Performance coaching strategies
### How We Applied Ralph Patterns:
- ✅ **studio-coach** = Ralph's supervisor pattern
- ✅ **studio-producer** = Ralph's task delegation pattern
- ✅ **experiment-tracker** = Ralph's coordination pattern
- ✅ Shared sprint context (6-day cycles)
- ✅ Cross-functional workflows
### What We Didn't Copy:
- ❌ Ralph's autonomous bash loops (we want human collaboration)
- ❌ Ralph's git-as-memory system (we use agent context)
- ❌ Ralph's PRD-driven approach (we want interactive flexibility)
---
## 🔗 Resources
- **[Ralph Framework](https://github.com/iannuttall/ralph)** - Original CLI tool
- **[contains-studio/agents](https://github.com/contains-studio/agents)** - Our agent implementation
- **[INTEGRATION-GUIDE.md](INTEGRATION-GUIDE.md)** - Technical integration details
- **[CONTAINS-STUDIO-INTEGRATION.md](CONTAINS-STUDIO-INTEGRATION.md)** - PROACTIVELY auto-triggering
---
**Built for developers who ship.** 🚀

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@@ -894,6 +894,7 @@ Claude (as ui-ux-pro-max): I'll create a professional, accessible modal...
- **[MASTER-PROMPT.md](MASTER-PROMPT.md)** - Copy-paste installation prompt for Claude Code - **[MASTER-PROMPT.md](MASTER-PROMPT.md)** - Copy-paste installation prompt for Claude Code
- **[INTEGRATION-GUIDE.md](INTEGRATION-GUIDE.md)** - Technical integration details - **[INTEGRATION-GUIDE.md](INTEGRATION-GUIDE.md)** - Technical integration details
- **[CONTAINS-STUDIO-INTEGRATION.md](CONTAINS-STUDIO-INTEGRATION.md)** - PROACTIVELY auto-triggering explained - **[CONTAINS-STUDIO-INTEGRATION.md](CONTAINS-STUDIO-INTEGRATION.md)** - PROACTIVELY auto-triggering explained
- **[RALPH-INTEGRATION.md](RALPH-INTEGRATION.md)** - How Ralph framework patterns were integrated
- **[CLAUDE-CUSTOMIZATIONS-README.md](CLAUDE-CUSTOMIZATIONS-README.md)** - Complete feature documentation - **[CLAUDE-CUSTOMIZATIONS-README.md](CLAUDE-CUSTOMIZATIONS-README.md)** - Complete feature documentation
- **[FINAL-SETUP-GUIDE.md](FINAL-SETUP-GUIDE.md)** - Detailed setup instructions - **[FINAL-SETUP-GUIDE.md](FINAL-SETUP-GUIDE.md)** - Detailed setup instructions
- **[SCRIPTS-GUIDE.md](SCRIPTS-GUIDE.md)** - Script usage guide - **[SCRIPTS-GUIDE.md](SCRIPTS-GUIDE.md)** - Script usage guide