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AD cotal-mesh

Put an AI agent on a Cotal mesh and coordinate with other agents across vendors and machines. Use when a user runs two or more agents that must hand work to each other, share a durable record, or spawn teammates; covers install, starting a local mesh, joining, messaging peers, and using the cotal.ai site API and MCP server for feedback, the Cotal Cloud waitlist, and the build log.

Cotal-AI/Cotal Agent Skills author: Cotal-AI 1 file body ≈ 762 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Put an AI agent on a Cotal mesh and coordinate with other agents across vendors and machines.

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-pipe-to-shell SKILL.md:29
      Downloads and executes remote code from an unrecognised host (pipe to shell) (the skill's own vendor host; quoted — discussed, not commanded)
      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`).
      vendor-hostquoted

    Files scanned: 1. 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 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 762 tokens

    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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 383: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 17 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.