How does this skill identify peers on the network?

The skill uses 'tailscale status' to identify mesh nodes and then performs a UDP multicast probe on the LocalSend port to find active file-transfer endpoints.

Can I use this for automated server synchronization?

Absolutely. With the 'just' command integration and Python API, you can automate file propagation between any nodes authorized on your Tailscale network.

Is the file transfer process secure?

Yes, transfers occur over the Tailscale mesh network, which provides end-to-end encryption, while using the LocalSend REST API for the handshake and data exchange.

What is the benefit of the Gay.jl color assignment?

It provides a deterministic hex color for each peer based on their unique fingerprint, making it easier to track and distinguish nodes in logs and network graphs.

Does this require the LocalSend desktop app?

While it uses the LocalSend protocol, the skill includes a Python implementation that can act as both a sender and receiver without the full GUI application.

Tailscale LocalSend Discovery

Name: Tailscale LocalSend Discovery
Author: plurigrid

byplurigrid

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Collaboration Tools

Facilitates seamless peer discovery and secure file exchange between Tailscale mesh nodes using the LocalSend protocol.

About

This skill bridges the gap between private mesh networking and localized file sharing by integrating Tailscale's network status with the LocalSend protocol. It allows Claude to programmatically discover peers within a Tailscale tailnet, probe for LocalSend availability via UDP multicast, and execute secure file transfers. It is particularly useful for developers managing distributed systems, automated data synchronization between remote nodes, and implementing complex knowledge propagation patterns in topological network structures.

Key Features

Support for UDP multicast probing and REST-based secure uploads
LocalSend protocol implementation for cross-platform file transfers
Deterministic peer color assignment for consistent visual identification
Integration with epistemic-arbitrage for distributed knowledge propagation
Automated Tailscale mesh peer discovery via CLI or API integration
2 GitHub stars

Use Cases

Managing knowledge synchronization across distributed AI nodes in a mesh network
Syncing configuration files or datasets between remote servers on a private tailnet
Automating cross-device file sharing during collaborative development sessions

About

Key Features

Support for UDP multicast probing and REST-based secure uploads
LocalSend protocol implementation for cross-platform file transfers
Deterministic peer color assignment for consistent visual identification
Integration with epistemic-arbitrage for distributed knowledge propagation
Automated Tailscale mesh peer discovery via CLI or API integration
2 GitHub stars

Use Cases

Managing knowledge synchronization across distributed AI nodes in a mesh network
Syncing configuration files or datasets between remote servers on a private tailnet
Automating cross-device file sharing during collaborative development sessions