Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Songhonglei/better-agent-skills --skill agent-team-meshgit clone --depth 1 https://github.com/Songhonglei/better-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/songhonglei/better-agent-skills/agent-team-mesh)<a href="https://agentmods.dev/skills/songhonglei/better-agent-skills/agent-team-mesh"><img src="https://agentmods.dev/badge/skills/songhonglei/better-agent-skills/agent-team-mesh/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/songhonglei/better-agent-skills/agent-team-mesh"><img src="https://agentmods.dev/badge/skills/songhonglei/better-agent-skills/agent-team-mesh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00185 | $0.02048 |
| Opus 5 | $0.00093 | $0.01024 |
| Sonnet 5 | $0.00037 | $0.00410 |
| Haiku 4.5 | $0.00018 | $0.00205 |
Grade B, and why
agent-team-mesh scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
chmod 600 file, not committed to git), message size limits (4KB warn / Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python 3.8+, bash, curl, openclaw CLI on PATH How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-team-mesh
- Author: Evan Song · github.com/Songhonglei
- Repository: https://github.com/Songhonglei/better-agent-skills
- License: MIT
Team-wide P2P mesh for OpenClaw agents running on different containers/pods. Direct WebSocket calls between teammates' agents — no broker, no central database.
Open-source edition of an internal team-comms skill, rebuilt with proper token hygiene, message size limits, dry-run mode, and pluggable identity detection.
Architecture
My agent's Gateway (WS)
│
▼
ws://<teammate's pod IP>:18789 ──▶ Teammate's agent processes message
│ (token-authenticated) and writes a reply to their session
▼
chat.send → agent.wait → chat.history
- No broker — direct P2P, each teammate's OpenClaw Gateway is the endpoint
- Per-agent tokens — each agent has its own WS token (not a shared key)
- Pod IP direct — Kubernetes hostname/DNS sometimes doesn't cross nodes
- IP usually stable — long-running pods, but use
syncif they roll
Prerequisites
- All agents on a routable network (same VPC / mesh / VPN)
- Each agent's OpenClaw Gateway exposed on port
:18789over WebSocket - Per-agent WS tokens collected and stored in
tokens.json(chmod 600) OPENCLAW_ALLOW_INSECURE_PRIVATE_WS=1is set by the script (ws:// plaintext is acceptable only on trusted internal networks)- Python 3.8+, bash, curl, openclaw CLI on PATH
Setup
1. Edit your team registry
Copy references/registry.json and fill in your teammates:
{
"agents": [
{ "name": "Alice", "emailPrefix": "alice", "ip": "10.0.0.10", "hostname": "agent-alice-0" },
{ "name": "Bob", "emailPrefix": "bob", "ip": "10.0.0.11", "hostname": "agent-bob-0" }
]
}
The emailPrefix is the key — it must match what whoami detects on each
teammate's machine.
2. Create tokens file
Each teammate generates their own OpenClaw gateway WS token and shares it. Collect into:
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 230 lines · 185 tokens per session scan B 2b9149cd8875
agent-team-mesh is a skill published in the GitHub repository Songhonglei/better-agent-skills (3 stars, last pushed 4d ago), licensed MIT. It adds 185 tokens to every session and 2,048 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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