real-a2a

🥈Silver

Real-a2a enables AI coding agents to communicate directly over a peer-to-peer network. Operations teams benefit from streamlined workflows, reduced latency, and improved collaboration between AI agents. It connects to existing AI coding tools and workflows, enhancing automation in software development.

2730Updated 1mo ago

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/eqtylab/real-a2a.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Define the scope and agents. Specify the project, module, and two AI agents with distinct roles (e.g., one for performance, one for testing). Use placeholders like [PROJECT_NAME], [MODULE_NAME], [AI_AGENT_1], and [AI_AGENT_2] in your prompt.","tip":"Ensure the agents have access to the same codebase and tools (e.g., linters, profilers). Use tools like GitHub Actions or VS Code’s AI extensions to provide the necessary context."},{"step":"Configure the communication protocol. Choose a protocol like WebSockets, gRPC, or a shared file system for real-time message passing. Include [COMMUNICATION_PROTOCOL] in your prompt to enforce synchronization.","tip":"For local development, use a shared directory to simulate peer-to-peer communication. For cloud environments, configure a secure message broker like RabbitMQ or Kafka."},{"step":"Run the session and monitor output. Execute the prompt in your AI coding tool (e.g., Claude Code, Cursor, or GitHub Copilot). Review the real-time exchange of messages, shared files, and conflict resolutions.","tip":"Use logging tools (e.g., `loguru`, `structlog`) to capture the session for post-mortem analysis. Look for patterns in conflicts or bottlenecks to refine future sessions."},{"step":"Integrate the results. Apply the optimized code, tests, and documentation to your workflow. Use version control (e.g., Git) to track changes and roll back if necessary.","tip":"Automate the process by setting up CI/CD pipelines to trigger real-a2a sessions on pull requests or scheduled intervals. Tools like Jenkins or GitLab CI can orchestrate this."}]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/eqtylab/real-a2a

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Set up a real-a2a peer-to-peer network between [AI_AGENT_1] and [AI_AGENT_2] to collaboratively debug and optimize the [PROJECT_NAME] codebase. [AI_AGENT_1] should focus on identifying performance bottlenecks in the [MODULE_NAME] module, while [AI_AGENT_2] should handle unit test generation for the same module. Ensure both agents share their findings in real-time and resolve conflicts autonomously. Use [COMMUNICATION_PROTOCOL] for synchronization.

Example Output

### Real-a2a Peer-to-Peer Network Debug Session Report
**Project:** E-Commerce API (v2.1.4)
**Module:** `payment_processor.py`
**Agents:** `Agent-Orchestrator` (Performance) & `Agent-Tester` (Testing)

**Session Start:** 2024-05-15 14:32:03 UTC
**Session End:** 2024-05-15 14:45:12 UTC
**Total Messages Exchanged:** 47
**Conflicts Resolved:** 3

#### Performance Analysis (Agent-Orchestrator → Agent-Tester)
- **Identified Bottleneck:** The `process_payment()` function in `payment_processor.py` spends 68% of its time validating card details against a third-party API (average latency: 1.2s per call).
- **Proposed Optimization:** Implemented a local caching layer for valid card patterns (reduced latency to 80ms).
- **Shared Metrics:** Before/after benchmarks showed a 15x improvement in throughput (from 120 to 1,800 transactions/minute).

#### Test Generation (Agent-Tester → Agent-Orchestrator)
- **Generated Test Suite:** 42 unit tests covering edge cases (e.g., expired cards, network failures, malformed inputs).
- **Detected Regression:** One test (`test_failed_payment_retry`) failed due to the caching layer’s TTL (time-to-live) setting. Agent-Orchestrator adjusted the TTL from 5 minutes to 10 minutes to resolve.
- **Test Coverage:** Increased from 68% to 92% (added 14 new tests).

#### Real-Time Collaboration Highlights
1. **Conflict Resolution:** Agent-Tester flagged a race condition in the caching layer when Agent-Orchestrator pushed an update. Both agents autonomously rolled back the change and re-synchronized using a shared state file.
2. **Knowledge Sharing:** Agent-Orchestrator shared a `performance_benchmark.json` file with Agent-Tester, which was used to prioritize test cases for high-latency scenarios.
3. **Final Output:** The optimized `payment_processor.py` (v2.1.5) was committed to the main branch with all tests passing and performance metrics documented in `performance_report.md`.

**Next Steps:** Schedule a follow-up session to integrate the changes into the staging environment and monitor for regressions.