tailscale-mesh

Tailscale mesh VPN for secure peer-to-peer networking. WireGuard-based overlay network with MagicDNS and ACLs.

16 stars

Best use case

tailscale-mesh is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Tailscale mesh VPN for secure peer-to-peer networking. WireGuard-based overlay network with MagicDNS and ACLs.

Teams using tailscale-mesh should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/tailscale-mesh/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/tailscale-mesh/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/tailscale-mesh/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How tailscale-mesh Compares

Feature / Agenttailscale-meshStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Tailscale mesh VPN for secure peer-to-peer networking. WireGuard-based overlay network with MagicDNS and ACLs.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Tailscale Mesh Skill

**Trit**: 0 (ERGODIC - mediates network topology)  
**Foundation**: Tailscale + WireGuard + DERP  

## Core Concept

Tailscale creates a mesh VPN:
- WireGuard encryption
- NAT traversal via DERP relays
- MagicDNS for hostname resolution
- ACLs for access control

## Common Commands

```bash
# Status
tailscale status
tailscale netcheck

# Connect/disconnect
tailscale up
tailscale down

# Send files
tailscale file cp file.txt hostname:

# SSH
tailscale ssh hostname

# Funnel (public exposure)
tailscale funnel 8080
```

## ACL Configuration

```jsonc
{
  "acls": [
    {"action": "accept", "src": ["group:dev"], "dst": ["*:*"]},
    {"action": "accept", "src": ["tag:server"], "dst": ["tag:db:5432"]}
  ],
  "tagOwners": {
    "tag:server": ["group:ops"],
    "tag:db": ["group:dba"]
  }
}
```

## GF(3) Integration

```python
def trit_from_connection(conn):
    """Map connection type to GF(3) trit."""
    if conn.type == "direct":
        return 1   # PLUS: optimal path
    elif conn.type == "derp":
        return 0   # ERGODIC: relayed
    else:
        return -1  # MINUS: failed/blocked
```

## Canonical Triads

```
bisimulation-game (-1) ⊗ tailscale-mesh (0) ⊗ localsend-mcp (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ tailscale-mesh (0) ⊗ tailscale-file-transfer (+1) = 0 ✓
```

## See Also

- `tailscale-file-transfer` - File transfer with open games semantics
- `localsend-mcp` - P2P transfer via LocalSend



## Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

### Graph Theory
- **networkx** [○] via bicomodule
  - Universal graph hub

### Bibliography References

- `graph-theory`: 38 citations in bib.duckdb
- `distributed-systems`: 3 citations in bib.duckdb

## Cat# Integration

This 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
```

### GF(3) Naturality

The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```

This ensures compositional coherence in the Cat# equipment structure.