jonhill90/vibes
If you are the rightful owner of vibes and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.
Vibes is a conversational development environment that leverages distributed MCP servers to transform Claude Desktop into an interactive platform for building and understanding code.
Vibes - Conversational Development Environment
Vibes transforms Claude Desktop into a conversational development environment through distributed MCP servers. Instead of learning command-line tools, you describe what you want to build and Claude implements it while teaching you.
Core Philosophy
Ask β Build β Understand β Improve β Create
Current Architecture
Production Stack: Claude Desktop + MCP Servers + Docker Containers + Vector Database
Vibes runs as a distributed system of specialized MCP servers, each handling specific capabilities:
Server | Purpose | Status | Connection |
---|---|---|---|
mcp-vibes-server | Shell access, container management | β Active | Docker exec |
mcp-vibesbox-server | Unified shell + VNC GUI | β Active | Docker exec |
basic-memory | Persistent memory across Claude sessions | β Active | Docker exec |
MCP_DOCKER | Container orchestration gateway | β Active | docker mcp |
archon | Task/knowledge management, RAG search | β Active | npx mcp-remote |
Quick Start
Prerequisites
- Docker Desktop
- Claude Desktop
- Git
1. Clone Repository
git clone https://github.com/jonhill90/vibes.git
cd vibes
docker network create vibes-network
2. Start MCP Servers
cd mcp/mcp-vibes-server
docker-compose up -d
cd ..
3. Configure Claude Desktop MCP Settings
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"vibesbox": {
"command": "docker",
"args": ["exec", "-i", "mcp-vibesbox-server", "python3", "/workspace/server.py"]
},
"basic-memory": {
"command": "docker",
"args": ["exec", "-i", "basic-memory-mcp", "/app/start.sh"]
},
"MCP_DOCKER": {
"command": "docker",
"args": ["mcp", "gateway", "run"]
},
"archon": {
"command": "npx",
"args": ["mcp-remote", "http://localhost:8051/mcp"]
}
}
}
4. Restart Claude Desktop
After updating the configuration, restart Claude Desktop to load the MCP servers.
Current Capabilities
-
Shell execution & container management via
mcp-vibes-server
MCP- Run bash commands in isolated environments
- Docker container lifecycle management
- Network access to vibes-network
-
Desktop automation & visual feedback via
mcp-vibesbox-server
MCP- VNC desktop environment (XFCE4, 1920x1080)
- Screenshot capture for Claude's vision
- Mouse/keyboard control (click, drag, type)
- ARM64 Chromium browser automation
-
Task & knowledge management via
archon
MCP- Task tracking (find_tasks, manage_task)
- RAG search across documentation (2-5 keyword queries)
- Project management and organization
-
Persistent memory via
basic-memory
MCP- Local Markdown-based knowledge storage
- Conversation context across sessions
- Semantic linking and knowledge graphs
-
Container orchestration via
MCP_DOCKER
gateway- Unified MCP server management
- Security isolation and secrets management
- Enterprise observability
Context Optimization
We compressed the context Claude sees by 59-70% without losing functionality.
File Sizes Achieved:
- CLAUDE.md: 107 lines (from 143, 25% reduction)
- Patterns: 47-150 lines each (target β€150)
- Commands: 202-320 lines each (target β€350)
Context Per Command:
/generate-prp
: 427 lines (59% reduction from 1044 baseline)/execute-prp
: 309 lines (70% reduction from 1044 baseline)
Impact: ~320,400 tokens saved annually (assuming 10 PRP workflows/month)
Why this matters: Claude has an "attention budget"βmore tokens doesn't mean better results. By compressing context, we improved both speed and accuracy.
See for detailed methodology.
Context Engineering & PRPs
Vibes uses Context Engineering to transform AI coding from generic code generation to production-ready implementation. Instead of prompting in the dark, we engineer comprehensive context that enables first-pass success.
What are PRPs?
PRP (Product Requirements Prompt) = PRD + Curated Codebase Intelligence + Agent Runbook
A PRP is a context engineering artifact that treats Claude like a competent junior developer who needs a comprehensive briefing. It includes:
- PRD: Clear goals, business value, success criteria
- Curated Context: Documentation URLs, file references, existing patterns, known gotchas
- Agent Runbook: Implementation blueprint, pseudocode, task list, validation gates
Naming Convention: PRPs follow a standardized naming convention documented in . Key rules:
- Use
prps/{feature_name}.md
(noprp_
prefix) - Initial PRPs use
INITIAL_
prefix (auto-detected) - Directory structure matches feature name
PRP Workflow
1. Create INITIAL.md β Describe your feature
2. /generate-prp β Research and create comprehensive PRP
3. /execute-prp β Implement with validation loops
4. Validation β Tests pass, code quality verified
5. Done β Production-ready code
Directory Structure
vibes/
βββ .claude/ # Context-engineered components (59-70% reduction)
β βββ commands/ # Slash commands for Claude Code
β β βββ generate-prp.md # 320 lines (59% reduction)
β β βββ execute-prp.md # 202 lines (70% reduction)
β β βββ list-prps.md
β β βββ prep-parallel.md
β βββ patterns/ # Reusable implementation patterns
β β βββ README.md # Pattern library index
β β βββ archon-workflow.md
β β βββ parallel-subagents.md
β β βββ quality-gates.md
β β βββ security-validation.md
β βββ agents/ # Subagent specifications
β βββ templates/ # Report templates
βββ prps/ # Per-feature PRP artifacts
β βββ {feature_name}/ # Feature-specific directory
β β βββ planning/ # Research outputs
β β βββ examples/ # Concrete examples
β β βββ execution/ # Implementation artifacts
β βββ templates/ # PRP templates
β βββ prp_base.md # Comprehensive base
β βββ feature_template.md # Standard features
β βββ tool_template.md # API integrations
β βββ documentation_template.md
βββ CLAUDE.md # 107 lines (project rules only)
βββ mcp/ # MCP server implementations
βββ mcp-vibesbox-server/
βββ mcp-vibes-server/
Core Principles
- Context is King: Include ALL necessary documentation, examples, and gotchas
- Validation Loops: Provide executable tests the AI can run and fix iteratively
- Information Dense: Use keywords and patterns from your codebase
- Progressive Success: Start simple, validate, then enhance
- One-Pass Implementation: Comprehensive context enables getting it right the first time
Available Commands
/generate-prp <file>
- Research codebase and create comprehensive PRP/execute-prp <prp>
- Execute PRP with iterative validation/list-prps [status]
- List PRPs by status (active/completed/archived)
Example Templates
Choose the right template for your task:
- Feature Template: Standard feature development
- Tool Template: API integrations and external tools
- Documentation Template: READMEs, API docs, guides
Learn More
- - Comprehensive template with all sections
- - Reusable implementation patterns
- Context Engineering Intro - Original philosophy
Philosophy: Context engineering is 10x better than prompt engineering and 100x better than vibe coding. Give Claude the context it needs to succeed.
Pattern Library
The .claude/patterns/
directory contains reusable implementation patterns extracted from the PRP system. These patterns enable consistent, high-quality implementations across all features.
Pattern | Purpose | Link |
---|---|---|
archon-workflow | Archon MCP integration, health checks, graceful degradation | |
parallel-subagents | 3x speedup through multi-task parallelization | |
quality-gates | Validation loops ensuring 8+/10 PRP scores | |
security-validation | 5-level security checks for user input |
See for complete pattern documentation and usage guidelines.
Future Vision
Phase 1: Observable Agent Execution (In Development)
- Real-time screen sharing of AI work (terminal, browser, Neovim)
- Agent-specific environments with persistent state
- Pause/resume execution controls
Phase 2: Team Collaboration
- Discord-like interface for human-AI teams
- Multi-user knowledge spaces
- Agent coordination visualization
Phase 3: Advanced Intelligence
- Cross-session agent learning and skill accumulation
- Intelligent task routing between specialized agents
- Predictive workflow assistance
Conversational development environment in production. Observable AI execution in development.