tailscale-mesh
Tailscale mesh VPN for secure peer-to-peer networking. WireGuard-based overlay network with MagicDNS and ACLs.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tailscale-mesh/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tailscale-mesh Compares
| Feature / Agent | tailscale-mesh | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.Related Skills
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