npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
Discover peers via Tailscale mesh and exchange files via LocalSend protocol.
Implements MCP tools for LocalSend P2P file transfers: discovery/advertising via NATS/Tailscale, session negotiation, transfer, and throughput tuning with spectral gap heuristic.
Manage Tailscale tailnet via CLI for local ops (status, ping, file transfer, serve/funnel/SSH) and API for tailnet-wide (devices, auth keys, online status, tags/routes).
Manages Tailscale mesh VPN networks via CLI (status, ping, netcheck, file transfer, serve, funnel) and API (devices, auth keys, DNS).
Share bugs, ideas, or general feedback.
Discover peers via Tailscale mesh and exchange files via LocalSend protocol.
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Tailscale API │────▶│ Peer Discovery │────▶│ LocalSend API │
│ (mesh network) │ │ (propagator) │ │ (file xfer) │
└─────────────────┘ └──────────────────┘ └─────────────────┘
tailscale status --json → get mesh peers# Discover peers on tailscale with localsend
just ts-localsend-discover
# Send file to peer
just ts-localsend-send <peer> <file>
# Receive mode
just ts-localsend-receive
from tailscale_localsend import TailscaleLocalSend
tls = TailscaleLocalSend(seed=0x6761795f636f6c6f)
# Discover peers
peers = tls.discover()
# [{'name': 'macbook', 'tailscale_ip': '100.x.x.x', 'localsend_port': 53317, 'color': '#A855F7'}]
# Send file
tls.send(peer='macbook', file='data.json')
# Receive (blocking)
tls.receive(callback=lambda f: print(f"Got {f}"))
tailscale status --json for mesh peersPOST /api/localsend/v2/prepare-uploadPOST /api/localsend/v2/upload?sessionId=...Each peer gets deterministic color from Gay.jl:
peer_color = gay_color_at(hash(peer_fingerprint) % 1000, seed=GAY_SEED)
from epistemic_arbitrage import ArbitrageNetwork
network = ArbitrageNetwork(seed=1069)
for peer in tls.discover():
network.add_cell(peer['name'], knowledge=peer.get('files', 0))
# Propagate knowledge between peers
network.add_propagator(:peer_sync, sources, targets)
network.run_parallel(n_workers=len(peers))
just ts-peers # List tailscale peers
just ls-peers # List localsend peers
just ts-ls-bridge # Bridge both networks
Base directory: ~/.codex/skills/tailscale-localsend
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
distributed-systems: 3 citations in bib.duckdbThis skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.