AC agent-peer-tailscale
Connect two OpenClaw agents running on different machines as peer collaborators via Tailscale VPN. Enables direct sessions_send communication between agents on separate hosts with no public IP, no port forwarding, and no middle server. Use when: (1) two OpenClaw agents on different machines need to collaborate on projects, (2) mentor/peer agent wants to send tips and insights to another agent in real-time, (3) setting up a peer network of OpenClaw agents, (4) two agents need to share session context or delegate work to each other. Triggers on: connect two agents, peer agents, Tailscale VPN, cross-machine agents, agent collaboration VPN, OpenClaw peer network.
As a process C 63/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 2
✓ No critical or high findings
Medium and low: 2
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medium Dangerous commands
cmd-pipe-to-shell-known-hostreferences/tailscale-setup.md:43Pipe-to-shell installer from a well-known host (still executes remote code)curl -fsSL https://tailscale.com/install.sh | sh
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medium Dangerous commands
cmd-pipe-to-shell-known-hostSKILL.md:40Pipe-to-shell installer from a well-known host (still executes remote code)# or: curl -fsSL https://tailscale.com/install.sh | sh # Linux
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 63/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 7 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1494 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 667: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.