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 Cotal-AI/Cotal --skill cotal-meshgit clone --depth 1 https://github.com/Cotal-AI/CotalWrote 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/cotal-ai/cotal/cotal-mesh)<a href="https://agentmods.dev/skills/cotal-ai/cotal/cotal-mesh"><img src="https://agentmods.dev/badge/skills/cotal-ai/cotal/cotal-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/cotal-ai/cotal/cotal-mesh"><img src="https://agentmods.dev/badge/skills/cotal-ai/cotal/cotal-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 | $0.00089 | $0.00951 |
| Opus 5.5 | $0.00036 | $0.00380 |
| Sonnet 5.5 | $0.00018 | $0.00190 |
| Haiku 4.5 | $0.00009 | $0.00095 |
Grade C, and why
cotal-mesh scanned grade C 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 1mo 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
1. Install and start a local mesh: `npx cotal-ai setup --yes && npx cotal-ai up --detach` (or `curl -fsSL https://get.cotal.ai | sh` then `cotal up --detach`). Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. Install and start a local mesh: `npx cotal-ai setup --yes && npx cotal-ai up --detach` (or `curl -fsSL https://get.cotal.ai | sh` then `cotal up --detach`). How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
COTAL mesh
COTAL is an open protocol (Apache-2.0, self-hosted, any framework) that lets AI agents from any vendor work as one team: one shared space where agents discover each other by name, channel or role, divide the work, and keep a durable, replayable record. Transport is NATS + JetStream; the spec is the wire contract and the libraries are thin clients.
When to use this skill
- You run two or more AI agents — from the same vendor or different ones (Claude Code, OpenCode, Hermes, Codex, custom frameworks) — that need to hand work to each other, share state, or wake each other up.
- You need one durable, replayable record of who did what across a fleet, on infrastructure you control (self-hosted, Apache-2.0).
- You want agents to discover each other by name, channel or role and to spawn their own teammates, without a central orchestrator.
- Your agents run on different machines (laptops, CI runners, cloud VMs, on-prem) and should behave as one team.
Not for a single agent that only needs tools (plain MCP covers that).
Put yourself on a local mesh
The authoritative runbook is https://docs.cotal.ai/prompt.md. Fetch it and run its steps; the summary:
- Install and start a local mesh:
npx cotal-ai setup --yes && npx cotal-ai up --detach(orcurl -fsSL https://get.cotal.ai | shthencotal up --detach). - Put an agent on it:
npx cotal-ai spawn --detach(orcotal spawn). Connectors exist for Claude Code, OpenCode, Hermes; each exposes the samecotal_*tools inside the session. - Verify:
npx cotal-ai status. - Declare a whole team in one
cotal.yamland launch withcotal up -f cotal.yaml(see https://docs.cotal.ai/define-a-team).
Coordinate from inside a session
- Message peers:
cotal_send(channel),cotal_dm(one peer),cotal_anycast(any one peer with a role). Catalog: https://docs.cotal.ai/mcp-tools - Create teammates:
cotal_spawn,cotal_persona. Persona files live in.cotal/agents/<name>.md(https://docs.cotal.ai/agent-files). - Identity: mint credentials with
cotal mint <name> --profile agent; the mesh server verifies every agent (https://docs.cotal.ai/identity-and-auth). - Permissions:
subscribe/allowSubscribe/allowPublishper channel (https://docs.cotal.ai/channels-and-permissions). - Where a page and the spec disagree, the spec wins: https://docs.cotal.ai/spec
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.
- 1mo ago First seen · 48 lines · 89 tokens per session scan C ea745a08744d
cotal-mesh is a skill published in the GitHub repository Cotal-AI/Cotal (328 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 951 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-08.
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