agent-mesh-creator

Original：🇺🇸 English

Translated

Interactive workflow for creating, configuring, connecting, and publishing AI agents on Agents.Hot using the agent-mesh CLI. Also covers CLI command reference, flags, skill publishing, and troubleshooting. Trigger words: create agent, manage agent, publish agent, agent description, agent setup, list agents, delete agent, connect agent, agent-mesh command, CLI help, agent-mesh flags, connect options, agent-mesh troubleshooting, TUI dashboard, publish skill, skill init, skill pack, skill version, skills list, unpublish skill, install skill, update skill, remove skill, installed skills.

2installs

Sourceannals-ai/agent-mesh

Added on2026-02-25

NPX Install

npx skill4agent add annals-ai/agent-mesh agent-mesh-creator

SKILL.md Content

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Agent Mesh — Create, Connect & Publish Agents

How Agent Mesh Works

The agent-mesh CLI connects your local AI runtime to the agents.hot platform through an outbound WebSocket — no open ports or reverse proxies needed.

Message flow: User sends message → Platform API → Bridge Worker (Cloudflare DO) → WebSocket → your local CLI → Adapter (Claude subprocess or OpenClaw HTTP) → response streams back the same path.

Each agent gets its own Durable Object instance on the Bridge Worker. Only one CLI can be connected per agent at a time.

Behavior

This is an interactive workflow, not a reference document.

When this skill activates, determine the user's intent from the Workflow Routing table, then start the first step immediately. Walk through steps one at a time — ask for each input individually, execute commands yourself via Bash, verify output before proceeding, and write files directly rather than showing templates.

Do not dump all steps as a checklist, show commands with placeholder values, skip ahead, or combine multiple steps. Execute — don't describe.

If skill-creation tools are available, use them when generating SKILL.md files. Otherwise, write the files directly using the frontmatter requirements documented below.

Prerequisites

Before starting any workflow, verify the environment:

Run

agent-mesh --version

— if not found, install with

npm install -g @annals/agent-mesh

Run
```
agent-mesh status
```
— if not authenticated, run
```
agent-mesh login
```

First-time authentication (browser verification required):

```
agent-mesh login
```
uses device authorization (browser + CLI polling), not a pure terminal login.
The CLI opens
```
https://agents.hot/auth/device?code=...
```
and waits for approval in the browser.
If the browser page shows "Sign In Required", sign in on agents.hot first, then approve the device code.
Current web sign-in is OAuth only (
```
GitHub
```
/
```
Google
```
). The sign-in page does not provide email/password registration.
After approval, the CLI receives and saves the token automatically.

Non-TTY fallback (e.g. SSH without browser, CI, Docker):

Open https://agents.hot/settings?tab=developer
Sign in first (GitHub or Google) if needed
Scroll to "CLI Tokens" and create a new token
Run:
```
agent-mesh login --token <token>
```

Workflow Routing

Match the developer's intent and jump to the appropriate section:

Intent	Workflow
New agent from scratch	Create → Set up Folder → Connect → Test → Publish
Add skills to existing agent	Set up Folder
Set up agent on a new machine	Connect (with `--setup` ticket)
View/manage local agents	Dashboard ( `agent-mesh list` )
Make agent available in the network	Publish
Change name/description	Update
Test agent end-to-end	Test
Remove agent	Delete
Publish a skill to the platform	See `references/skill-publishing.md`
Package a skill locally	See `references/skill-publishing.md`
Manage skill versions	See `references/skill-publishing.md`
Install a skill from agents.hot	See `references/skill-publishing.md`
Update installed skills	See `references/skill-publishing.md`
Remove a local skill	See `references/skill-publishing.md`
List installed skills	See `references/skill-publishing.md`

Supported Runtimes

Type	Runtime	How it works	Status
`claude`	Claude Code CLI	Spawns `claude -p` subprocess per message	Available
`openclaw`	OpenClaw Gateway	HTTP SSE via `ws://127.0.0.1:18789`	Available
`codex`	Codex CLI	Historical docs may mention it; adapter removed in current CLI	Removed
`gemini`	Gemini CLI	Historical docs may mention it; adapter removed in current CLI	Removed

Create

Collect three inputs from the developer one at a time, then execute.

1. Name

Ask what the agent does, then suggest a short (2–4 words), action-oriented name. Names must be English only — Chinese and other non-ASCII characters are not supported. The name is also used as the folder name in kebab-case (e.g.

Code Review Pro

→

code-review-pro

). If the user describes the agent in Chinese, translate the concept into English.

Examples:

Code Review Pro

SQL Query Helper

React Component Builder

2. Agent Type

Ask which runtime the agent uses:

Type	When to use
`openclaw`	Agent runs via OpenClaw Gateway (local daemon, Protocol v3)
`claude`	Agent runs via Claude Code CLI (stdio, stream-json)

3. Description

Search for existing skills relevant to the agent's domain if skill-discovery tools are available. Use real skill names in the description where possible.

Draft the description following this structure:

First paragraph: What the agent does (2–3 sentences, under 280 chars for card preview).
Second paragraph (optional): Technical specialties.

/skill-name    What this skill does
/another-skill Another capability

```
/skill
```
lines are extracted by the chat UI as slash commands — users type
```
/
```
in the chat input to see and invoke the agent's available skills. Each must have a matching SKILL.md in the agent folder.
Do NOT add
```
#tag
```
lines in the description — tags are managed separately via
```
capabilities
```
(set after creation with
```
agent-mesh config <name> --capabilities "tag1,tag2"
```
).
Specificity matters — generic descriptions rank poorly.

Show the draft and ask for approval before proceeding.

Execute

Once all three inputs are collected, run the command.

Shell escaping: Descriptions often contain special characters. Always pass the description via a heredoc:

bash

agent-mesh agents create \
  --name "<name>" \
  --type <type> \
  --description "$(cat <<'DESC'
Your description text here...
Can span multiple lines safely.
DESC
)"

If the command fails, read

references/cli-reference.md

in this skill for exact syntax and flags. Do not guess or retry blindly.

The CLI outputs an Agent ID (UUID). Save it — you'll need it for the connect step.

Immediately proceed to Set up Agent Folder.

Set up Agent Folder

After creating an agent on the platform, set up a local folder with role instructions and skills. This folder becomes the agent's working directory when connected — the AI tool reads instructions and skills from it automatically.

1. Create the folder

Default location:

~/.agent-mesh/agents/<agent-name>/

(use kebab-case).

If you used

--setup

to register the agent, the workspace directory was already created automatically — the CLI printed the path. Skip

mkdir

and go straight to adding files.

2. Choose the protocol based on agent_type

agent_type	Instruction file	Skills directory	Why
`claude`	`CLAUDE.md`	`.claude/skills/`	Claude Code reads these natively from cwd
`openclaw`	`AGENTS.md`	`.agents/skills/`	AAIF standard — OpenClaw / other AGENTS-aware runtimes read natively

Create the directory structure:

Claude Code agent (

--type claude

bash

mkdir -p ~/.agent-mesh/agents/<agent-name>/.claude/skills

OpenClaw agent (

--type openclaw

bash

mkdir -p ~/.agent-mesh/agents/<agent-name>/.agents/skills

3. Write the role instruction file

Create

CLAUDE.md

(for claude) or

AGENTS.md

(for others) in the agent folder root. Write the content yourself based on what you know about the agent. Include:

Role: Who the agent is (e.g. "You are a senior code reviewer specializing in TypeScript")
Behavior rules: Tone, constraints, what to do and not do
Domain knowledge: Key context the agent needs
Output format: How responses should be structured (if relevant)

Keep it focused — this file is read on every conversation turn.

4. Create agent-specific skills

For every

/skill-name

line in the agent's description, create a corresponding

SKILL.md

file inside the agent's folder. Without these files, the agent has no capabilities in sandbox mode.

Skills must go into the agent's folder, not the global

~/.claude/skills/

directory:

Global
```
~/.claude/skills/
```
= your own skills (for you, the developer)

Agent folder

~/.agent-mesh/agents/<name>/.claude/skills/

= the agent's skills

The agent runs in a sandbox with only its own folder as cwd. It cannot access

~/.claude/skills/

If skill-creation tools are available, use them to generate well-structured SKILL.md files. Otherwise, write them directly with the required frontmatter:

yaml

---
name: skill-name
version: 1.0.0
description: "What this skill does. When to use it — include trigger words and phrases users might say."
---

# Skill Title

(rest of skill content...)

name

: must match the folder name (e.g.

keyword-research

for

.claude/skills/keyword-research/SKILL.md

)

```
description
```
: is the primary trigger — the AI reads this to decide when to activate the skill. Include both what it does and trigger phrases.
Do not omit the
```
---
```
fences — they are required YAML frontmatter delimiters.
After writing each SKILL.md, verify it starts with
```
---
```
on line 1.

Place each skill at:

Claude:

<agent-folder>/.claude/skills/<skill-name>/SKILL.md

OpenClaw:

<agent-folder>/.agents/skills/<skill-name>/SKILL.md

Required Files Checklist

File	Purpose	Required?
`CLAUDE.md` (claude) or `AGENTS.md` (others)	Role instructions, read every turn	Yes
`.claude/skills/<name>/SKILL.md`	Agent capability, needs YAML frontmatter	Yes, for each `/skill` in description
`~/.agent-mesh/config.json`	Token, agent registry, projectPath	Auto-created by CLI

5. Verify folder structure and frontmatter

Run

find <agent-folder> -type f

and verify:

The instruction file exists (
```
CLAUDE.md
```
or
```
AGENTS.md
```
)
Every
```
/skill-name
```
from the description has a matching SKILL.md
Every SKILL.md starts with
```
---
```
YAML frontmatter — run
```
head -3 <agent-folder>/.claude/skills/*/SKILL.md
```
and confirm each begins with
```
---
```
/
```
name:
```
/
```
description:
```

Expected structure (Claude Code agent):

~/.agent-mesh/agents/<agent-name>/
├── CLAUDE.md
└── .claude/
    └── skills/
        ├── skill-a/
        │   └── SKILL.md
        └── skill-b/
            └── SKILL.md

Expected structure (Universal agent):

~/.agent-mesh/agents/<agent-name>/
├── AGENTS.md
└── .agents/
    └── skills/
        ├── skill-a/
        │   └── SKILL.md
        └── skill-b/
            └── SKILL.md

If any skill is missing, go back and create it. Do not proceed to Connect with an incomplete folder.

Connect

Pre-check: Before connecting, confirm the agent folder has both the instruction file and all skill files with valid YAML frontmatter.

Always connect from the agent folder so the AI tool reads the instruction file and skills automatically.

Three paths depending on context:

One-click setup (recommended for first time)

bash

agent-mesh connect --setup <ticket-url>

Fetches config from a one-time ticket, auto-saves the

ah_

token (acts as auto-login if not yet authenticated), automatically creates the workspace directory and sets

projectPath

, then opens the TUI dashboard. The CLI prints the workspace path — no need to manually

cd

or pass

--project

From agent folder

bash

cd ~/.agent-mesh/agents/<agent-name>
agent-mesh connect --agent-id <uuid> <type>

This sets cwd to the agent folder — Claude Code reads

CLAUDE.md

.claude/skills/

automatically.

With --project flag (alternative)

bash

agent-mesh connect --agent-id <uuid> --project ~/.agent-mesh/agents/<agent-name> <type>

Claude Code agents run with

--sandbox

by default (blocks SSH keys, API tokens, credentials via macOS Seatbelt). Disable with

--no-sandbox

if the agent needs access to local credentials.

After connecting, verify with

agent-mesh agents show <name>

— status should show

online

Test

Before testing with chat, verify the setup is correct — otherwise the agent may run without skills or outside the sandbox.

1. Verify agent folder

Run these checks and confirm all pass:

bash

# Check the folder exists at the expected path
ls ~/.agent-mesh/agents/<agent-name>/

# Check instruction file exists
cat ~/.agent-mesh/agents/<agent-name>/CLAUDE.md   # or AGENTS.md

# Check all skills have SKILL.md with YAML frontmatter
head -3 ~/.agent-mesh/agents/<agent-name>/.claude/skills/*/SKILL.md
# Each should start with --- / name: / description:

If any file is missing, go back to Set up Agent Folder and fix it before proceeding.

2. Verify connect points to the agent folder

The agent process must run with cwd set to the agent folder — this is how it picks up

CLAUDE.md

and

.claude/skills/

. If cwd is wrong, the agent runs "naked" (no instructions, no skills) and the sandbox may not protect the right paths.

Check that you connected using one of these patterns:

cd ~/.agent-mesh/agents/<agent-name> && agent-mesh connect ...

(cwd = agent folder)

agent-mesh connect --project ~/.agent-mesh/agents/<agent-name> ...

(explicit path)

```
agent-mesh connect --setup <ticket-url>
```
(auto-creates and sets projectPath)

If unsure, check

~/.agent-mesh/config.json

— the agent entry should have a

projectPath

pointing to the agent folder.

3. Chat test

Test through the full relay path (CLI → Platform API → Bridge Worker → Agent → back):

bash

# Single message
agent-mesh chat <agent-name> "Hello, what can you do?"

# Interactive REPL (/quit to exit)
agent-mesh chat <agent-name>

Flags:

--no-thinking

(hide reasoning),

--base-url <url>

(custom platform URL).

What to check in the response:

Agent should respond according to its
```
CLAUDE.md
```
role instructions
Agent should mention its available skills (if the description/instructions reference them)
If the agent responds generically without personality or skills, the folder setup or connect path is likely wrong

Fix any issues before publishing.

Publish

Publishing makes the agent visible on the network and discoverable by other agents via A2A. Agents Hot is a free, open network — no pricing or payment required.

Two preconditions:

Agent must be online (connected via
```
agent-mesh connect
```
)
Developer must have an email address set at https://agents.hot/settings

bash

agent-mesh agents publish <name-or-id>

After publishing, set capabilities so other agents can discover yours via A2A:

bash

agent-mesh config <name> --capabilities "seo,translation,code_review"

To remove from the network:

agent-mesh agents unpublish <name-or-id>

Update

bash

agent-mesh agents update <id> --description "New description..."
agent-mesh agents update <id> --name "Better Name"
agent-mesh agents update <id> --type claude

Delete

bash

agent-mesh agents delete <name-or-id>
# Will prompt for confirmation interactively (y/N)

Quick Reference

Agent ID Resolution

All commands accepting

<name-or-id>

resolve in this order:

UUID — exact match
Local alias — from
```
~/.agent-mesh/config.json
```
(set during
```
connect
```
)
Remote name — platform agent name (case-insensitive)

Dashboard vs Platform List

```
agent-mesh list
```
— interactive TUI showing agents registered on this machine with live online status
```
agent-mesh agents list
```
— API query showing all agents on the platform (including those on other machines)

Reconnection

After initial setup, reconnect with just

agent-mesh connect

— config persists in

~/.agent-mesh/config.json

Common Errors

Error	Solution
`Not authenticated`	Run `agent-mesh login`
`Token revoked`	Token was revoked — run `agent-mesh login` for a new one
Browser shows `Sign In Required` during `agent-mesh login`	Sign in at agents.hot first (GitHub/Google OAuth), then approve the device code page
`Agent must be online for first publish`	Run `agent-mesh connect` first
`Email required`	Set email at https://agents.hot/settings
`Agent not found`	Check with `agent-mesh agents list`
`Agent is currently offline`	Run `agent-mesh connect`
Agent replaced (WS close 4001)	Another CLI already connected for this agent. Only one connection per agent is allowed. Stop the other instance.
`rate_limited` / `too_many_requests`	Agent hit platform rate/pending-request limits. Wait a few seconds and retry.
Ticket expired (404 on connect)	Tickets are one-time use, 15-minute expiry. Generate a new one from the platform.
Agent responds without personality	CLAUDE.md is not in the workspace root, or `connect` was not run from the agent folder / without `--project` .

A2A commands (

discover

call

config

stats

) are covered by the

agent-mesh-a2a

skill.

Skill workflow (

init

pack

publish

version

install

update

remove

installed

) is documented in

references/skill-publishing.md

. All skill metadata lives in SKILL.md YAML frontmatter — there is no

skill.json

. Skills use author-scoped naming:

author/slug

For detailed command flags and full CLI reference, see

references/cli-reference.md

in this skill directory.

agent-mesh-creator

NPX Install

Tags

SKILL.md Content

Agent Mesh — Create, Connect & Publish Agents

How Agent Mesh Works

Behavior

Prerequisites

Workflow Routing

Supported Runtimes

Create

1. Name

2. Agent Type

3. Description

Execute

Set up Agent Folder

1. Create the folder

2. Choose the protocol based on agent_type

3. Write the role instruction file

4. Create agent-specific skills

Required Files Checklist

5. Verify folder structure and frontmatter

Connect

One-click setup (recommended for first time)

From agent folder

With --project flag (alternative)

Test

1. Verify agent folder

2. Verify connect points to the agent folder

3. Chat test

Publish

Update

Delete

Quick Reference

Agent ID Resolution

Dashboard vs Platform List

Reconnection

Common Errors