Agent Prompt Train

The Open Source System That Makes Claude Code Work for Development Teams

TL;DR: We’ve open-sourced Agent Prompt Train, a high-performance proxy system that transforms Claude Code from a single-developer tool into a collaborative development platform. It records conversations, provides AI-powered analysis, and enables knowledge sharing across engineering teams. Perfect for startups and small dev teams looking to 2-3x their development velocity with autonomous AI coding.

Note: In order to comply with the Anthropic Terms of Service, you need to have a Claude subscription for each user of Agent Prompt Train. This community-maintained tool interoperates with Anthropic’s Claude Code. It is not affiliated with, sponsored, or endorsed by Anthropic. Claude and Claude Code are trademarks of Anthropic.

The Claude Code Problem Every Developer Faces

Claude Code represents a fundamental shift in AI-assisted development. Unlike GitHub Copilot or Cursor, which function as intelligent autocomplete tools, Claude Code is designed with support for autonomous development—you describe what you want built, and it architects, codes, tests, and deploys complete features without constant supervision.

But here’s the challenge: Claude Code’s effectiveness depends entirely on how you prompt it, configure it, and structure your development workflow around it. But there’s another fundamental challenge: vibe coding, the intuitive, flow-state development that Claude Code enables, has traditionally been a solo endeavor. The insights, failures, and breakthroughs that come from hours of autonomous AI development sessions remain trapped in individual developer experiences, never making it to the rest of the team.

The difference between a 10x developer using Claude Code and someone getting mediocre results isn’t just coding skill, it’s accumulated knowledge about AI development best practices, prompting strategies, and debugging autonomous AI agents when they go off track.

Why We Built Agent Prompt Train: From Research to Reality

Through our AI development work, we’ve been exploring how to maximize the potential of AI assistants and agents across development teams. While Claude Code can operate autonomously for extended periods, it requires carefully structured prompts, clear documentation, and specialized tools—expertise that shouldn’t remain locked within individual workflows.

Agent Prompt Train emerged from this need to systematize and share knowledge across our different projects and Claude Code Accounts (one per developer). The dashboard allows engineers to review their AI conversations, understand successful patterns, and learn from less effective approaches. Over time, it evolved to analyze each Claude Code interaction, providing intelligent feedback for continuous improvement.

What Agent Prompt Train actually provides:

• Complete conversation logging and visualization for review and analysis
• AI-powered session analysis that identifies what worked and suggests improvements
• Team collaboration features through dashboard sharing and Slack integration

Agent Prompt Train doesn’t add new capabilities to Claude Code itself. Instead, it provides the infrastructure and analysis tools that help teams use Claude Code more effectively.

Core Features: Turning AI Development Transparent and Improvable

Agent Prompt Train operates as a transparent layer between your team and Claude Code, capturing and analyzing every interaction without changing how Claude Code itself works. The system is built around four core capabilities that transform individual AI tool usage into systematic team development practices.

1. Complete Visibility and Real-Time Monitoring

Every Claude Code session gets automatically logged, visualized, and shared via Slack integration. Instead of losing track of complex multi-hour autonomous coding sessions, you get:

• Real-time access to conversations, tool invocations, and prompts plus the ability to follow conversation branches and see sub-agents in action for effective troubleshooting
• Visual conversation maps showing how Claude Code spawns sub-agents and handles concurrent tasks
• Token usage tracking
• Comprehensive dashboard with filtering, search, and detailed session breakdowns

2. Historical Analytics and Pattern Recognition

The system provides comprehensive activity history that enables:

• Usage monitoring across team members and projects
• User Driven Pattern identification in successful vs. failed development sessions
• Performance metrics tracking conversation length, token usage, and success rates
• Continuous improvement insights based on historical data analysis

3. AI-Powered Development Session Analysis

This is where Agent Prompt Train gets powerful: we use AI to analyze AI development sessions. After each Claude Code conversation, you get:

• Performance insights: Topic summaries, sentiment analysis, and effectiveness metrics
• Actionable improvement tips: Specific suggestions for better prompting and configuration
• Prompt optimization recommendations: AI-generated suggestions for more effective future sessions
• Best practice identification: Patterns that lead to successful autonomous development

Example analysis output: “Provide specific PR numbers instead of relying on assistant interpretation. Use ‘rg’ command for faster file searches. Consider scoping context to specific modules for better performance.”

4. Team Knowledge Sharing Through Slash Commands

The real breakthrough is collaborative learning. Our slash command system lets developers create reusable prompts that incorporate team-wide best practices:

• Feature command: Automatically creates git branches, implements features, runs tests, and submits pull requests
• Review command: Handles code review processes and CI validation
• Cleanup command: Manages repository maintenance and removes artifacts from previous AI sessions

When one developer discovers an optimal prompting strategy, it becomes available to the entire team through shared command libraries.

See It In Action: Live Demo

Experience Agent Prompt Train in action with our live demo:

👉 https://prompttrain-demo.moonsonglabs.dev/dashboard

Note: This is a read-only demo showcasing real usage data from our development team.

The demo shows exactly how the conversation tracking, analytics dashboard, and AI analysis features work with real Claude Code sessions from our production environment.

Results: What up to 2-3x Development Velocity Actually Looks Like

Our engineering team can be running up to 3-4 concurrent Claude Code sessions, each tied to a separate Claude Code account, handling different and independent aspects of our codebase simultaneously.

However, achieving this level of velocity isn’t possible across all projects yet. Many existing codebases lack the structured documentation and decision-making context that Claude Code needs to avoid repeating past mistakes or making architectural missteps. This is why we’ve found Agent Prompt Train most effective with younger projects where we can shape the repository structure from the ground up.

Key improvements include:

• Technology agnostic development: Engineers can work effectively with unfamiliar frameworks and languages
• Autonomous feature development: 1-3 hour hands-off development sessions that produce production-ready code
• Accelerated learning curve: New team members become productive with Claude Code in days, not weeks
• Shared debugging knowledge: Complex AI development issues get solved once and shared across the entire team

Agent Prompt Train doesn’t change Claude Code, it makes teams more systematic about improving their use of it by making each session more effective.

Built by AI, for AI Development

Here’s something that makes this project unique: Agent Prompt Train has been entirely “vibe coded” using Agent Prompt Train itself. Our goal was to not manually touch a single file—proving that autonomous AI development can build production-ready systems for internal usage.

This isn’t just a tool for AI development; it’s a demonstration of what’s possible when you fully embrace autonomous AI coding practices.

Open Source Release: Agent Prompt Train on GitHub

We’re open-sourcing Agent Prompt Train because we believe the future of software development depends on teams learning to work effectively with autonomous AI systems. The codebase includes:

• High-performance proxy server optimized for individual Claude Code Max accounts
• Comprehensive monitoring with real-time conversation tracking
• Web dashboard for conversation visualization and analysis
• Slack integration for real-time team collaboration
• AI analysis engine for improving development practices
• Slash command framework for building reusable prompts
• Docker deployment for easy team setup

Perfect for Small Development Teams

Agent Prompt Train is designed for startups and small engineering teams (4-15 developers) who want to maximize their development capacity without hiring additional engineers. If you’re already using Claude Code individually, Agent Prompt Train transforms it into a collaborative development platform.

Requirements: Each developer must have their own unique Claude Max plan subscription.

Installation and Getting Started

The complete system is available on GitHub with documentation for:

• Docker-based deployment
• Claude API configuration and Max plan setup
• Slack integration setup
• Custom slash command development
• Team onboarding best practices
• Dashboard configuration and monitoring setup

The Future of AI-Powered Development Teams

At Moonsong Labs, we’re exploring new ways for humans and AI to collaborate on complex software projects because we hold a belief that teams that master autonomous AI development early will hold a decisive edge.

Agent Prompt Train is our effort to democratize that advantage. We’re not just building better tools—we’re discovering new ways for humans and AI to collaborate on complex software development. Every team that deploys Agent Prompt will uncover new optimization strategies, prompting techniques, and workflow improvements.

The teams that figure out autonomous AI development first will gain an insurmountable advantage. Agent Prompt Train is our contribution to making that knowledge accessible to every development team.

Resources & Next Steps

• GitHub Repository → Moonsong-Labs/agent-prompttrain
• Live Demo → https://prompttrain-demo.moonsonglabs.dev/dashboard

Get in Touch

Agent Prompt Train is an open source project from Moonsong Labs, turning Claude into a team-wide development platform with visibility, control, and intelligence built in.

📬 Interested in building with us? Contact us here.

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Agent Prompt Train represents the next evolution in AI-assisted development—from individual tools to collaborative systems. Join the community of teams transforming how software gets built.

⚠️ Use at your own risk: Like the autonomous AI development it enables, Agent Prompt Train pushes the boundaries of what’s possible with current AI technology.