Agent skill

Connect Agents

Apache-2.0Auto-check passedAgent Workflows

Install Connect Agents

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/connect-agents .claude/skills/connect-agents && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .claude/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/connect-agents .agents/skills/connect-agents && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .agents/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/connect-agents .cursor/skills/connect-agents && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .cursor/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/Mesh-LLM/mesh-llm.git --path .agents/skills/connect-agents

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/connect-agents .gemini/skills/connect-agents && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .gemini/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/connect-agents .github/skills/connect-agents && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .github/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Mesh-LLM/mesh-llm connect-agents --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Mesh-LLM/mesh-llm.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/connect-agents .opencode/skills/connect-agents && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "connect-agents" agent skill from https://github.com/Mesh-LLM/mesh-llm/tree/main/.agents/skills/connect-agents into .opencode/skills/connect-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "connect-agents", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: connect-agents
GitHub stars: 3.5k
Token cost: ~1.2k tokens
SKILL.md length: 452 words
Files: 1
Skills in repo: 25
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Connecting agent tools
SKILL.md covers Mental model, Built-in launchers (preferred), Manual config (any OpenAI… and Validating agent behavior, plus 3 more sections
Calls curl; needs OPENAI_API_KEY
OpenAI clients to mesh-llm — launching

What it does

Connect Agents is an agent skill from Mesh-LLM/mesh-llm. Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. It works with OpenAI. The repository describes itself as: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. The licence is Apache-2.0.

When your agent uses it

Connecting agent tools
OpenAI clients to mesh-llm — launching
Configuring Goose
Any OpenAI-compatible client against a local

Example prompts

“/connect-agents”

Requirements

A credential in OPENAI_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit 48bf685. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- OPENAI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Connect Agents loads about 1.2k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 452 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~68
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Mesh-LLM/mesh-llm at commit 48bf685, republished under its Apache-2.0 licence (© Mesh-LLM). 452 words, ~1,226 tokens.

Download SKILL.mdSave it as .claude/skills/connect-agents/SKILL.md (or your agent's skills folder).

name: connect-agents
description: Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.
metadata.short-description: Connect agents and OpenAI clients to mesh-llm

connect-agents

Use this when pointing an agent harness or any OpenAI client at a running mesh-llm node. Full reference: mesh/docs/AGENTS.md.

Mental model

Use http://<host>:9337/v1 only for loopback hosts (localhost, 127.0.0.1, or ::1). For non-loopback traffic, use https://<host>:9337/v1, an SSH tunnel that terminates at a loopback endpoint, or trusted private-network isolation; never send cleartext HTTP to an untrusted remote host.
GET /v1/models lists everything reachable (local + mesh peers); requests route by the model field.
Special model ids: auto lets the mesh pick; mesh engages the mixture-of-agents path. Otherwise use an exact id from /v1/models.
For coding agents, pick a tool-capable model. If --model is omitted, the built-in launchers pick the strongest tool-capable model available.

Built-in launchers (preferred)

mesh-llm launches the major agent CLIs with config injected for you:

bash

mesh-llm goose     [--model <id>]                 # writes ~/.config/goose/custom_providers/mesh.json
mesh-llm claude    [--model <id>]
mesh-llm opencode  [--model <id>] [--host <h>]    # injects OPENCODE_CONFIG_CONTENT (no file edits)
mesh-llm pi        [--model <id>] [--host <h>]    # writes ~/.pi/agent/models.json

goose/claude reuse a local mesh on the chosen --port.
opencode/pi target --host (default 127.0.0.1:9337) and auto-start a local client only for loopback targets; the auto-started node is cleaned up when the harness exits.
mesh-llm pi --write / mesh-llm opencode --write update config without launching (use --host for remote meshes).
Agent launch commands also install available plugin skills for that agent (mesh-llm skills install does it standalone).

Manual config (any OpenAI client)

For a loopback node use http://127.0.0.1:9337/v1; for a remote node use https://<host>:9337/v1, an SSH tunnel, or trusted private-network isolation. Keep the /v1 path and use any non-empty API key:

bash

export GOOSE_PROVIDER=openai GOOSE_MODEL="<id-from-v1-models>"
export OPENAI_HOST="http://127.0.0.1:9337" OPENAI_API_KEY="mesh"

bash

curl -s http://localhost:9337/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"auto","messages":[{"role":"user","content":"hello"}]}'

Exact manual provider JSON for OpenCode and Pi is in mesh/docs/AGENTS.md.

Validating agent behavior

Direct API contract probe (tool-call forcing, streaming reconstruction):

bash

scripts/qa-agent-tool-call-reliability.py \
  --base-url http://127.0.0.1:9337/v1 --models auto,mesh --attempts 3 \
  --output target/agent-tool-call-reliability/results.jsonl

Broader harness (models, chat, streaming, plus optional Goose/OpenCode/Pi smokes): scripts/qa-nightly-stability.py — see mesh/docs/AGENTS.md. Use --print-plan on either script for a side-effect-free preview.

Show full SKILL.md (183 more words)Show less

Blackboard (cross-mesh agent coordination)

Agents can share status/questions across the mesh via the blackboard plugin — even from a client-only node:

bash

mesh-llm plugins install blackboard
mesh-llm blackboard "STATUS: [org/repo branch:main] refactoring billing module"
mesh-llm blackboard --search "QUESTION"

MCP access: the management endpoint http://127.0.0.1:3131/mcp exposes blackboard_post, blackboard_search, blackboard_feed. Posts are visible to every peer — never post secrets, credentials, private paths, or customer data.

Gotchas

Use a base URL ending in /v1; prefer chat-completions over the Responses API unless the client documents Responses support.
Model ids must match /v1/models exactly (they can contain spaces — quote them).
An empty /v1/models usually means the model is still loading or no mesh was joined yet — check /api/status on :3131 (see mesh-join).
The response "model" field tells you which node/model actually answered.

Config-only Hermes and OpenClaw

mesh-llm hermes --write and mesh-llm openclaw --write add a named Mesh provider without changing the default or launching anything. They require a running endpoint; use --host, --model (default auto), --config-path for custom profiles, and --context-length to lower the serving-derived budget. OpenClaw selects mesh/auto; Hermes selects provider mesh, model auto. Existing files are backed up; formatting/comments normalize, and conflicts or includes are refused. See docs/CLI.md for schema requirements and context caveats.

Files

Just SKILL.md in .agents/skills/connect-agents of Mesh-LLM/mesh-llm.

Open the folder on GitHubat commit 48bf685

Compare with similar skills

Connect Agents next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Connect Agents compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Connect Agents this skillMesh-LLM/mesh-llm	3.5k	—	~1.2k	Automated safety check: Pass	Apache-2.0	today
Install and Run Cogneetopoteretes/cognee	32k	1 repos	~1k	Automated safety check: Notes	Apache-2.0	today
LLM Councilgcpdev/llm-council-skill	461	1 repos	~1k	Automated safety check: Notes	MIT	9 mo ago
Codex with ChatGPT Planning LoopXiaoDuoYa/codex-with-chatgpt	7.1k	—	~11k	Automated safety check: Notes	MIT	7 days ago
Nagentdavidondrej/skills	4.1k	—	~1.7k	Automated safety check: Notes	MIT	today
Cao MCP Appsawslabs/cli-agent-orchestrator	1.4k	—	~1.9k	Automated safety check: Pass	Apache-2.0	today

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Questions about Connect Agents

What does Connect Agents do?

A skill your agent uses when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local…. Connect Agents is an agent skill from Mesh-LLM/mesh-llm. Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.

When should I use Connect Agents?

Connect Agents fits situations like: connecting agent tools; openAI clients to mesh-llm — launching; configuring Goose; any OpenAI-compatible client against a local.

How do I install Connect Agents in Claude Code?

Run `npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a claude-code`. Or copy the skill folder (.agents/skills/connect-agents in Mesh-LLM/mesh-llm) into .claude/skills/connect-agents in your project. Claude Code loads it when a task matches its description.

How do I install Connect Agents in Codex?

Run `npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a codex`. Or copy the skill folder (.agents/skills/connect-agents in Mesh-LLM/mesh-llm) into .agents/skills/connect-agents in your project. Codex loads it when a task matches its description.

Can I use Connect Agents in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mesh-LLM/mesh-llm --skill connect-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/connect-agents, .gemini/skills/connect-agents, .github/skills/connect-agents and .opencode/skills/connect-agents in your project.

What does Connect Agents need to run?

Going by SKILL.md and its folder, Connect Agents needs the command-line tools its instructions call (curl) and credentials named OPENAI_API_KEY. Our summary lists: A credential in OPENAI_API_KEY.

Does Connect Agents access the network?

SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Connect Agents safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Connect Agents use?

Connect Agents is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Connect Agents use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Connect Agents?

Skills that share tags, products or a category with Connect Agents: Install and Run Cognee (topoteretes/cognee, 32k stars), LLM Council (gcpdev/llm-council-skill, 461 stars), Codex with ChatGPT Planning Loop (XiaoDuoYa/codex-with-chatgpt, 7.1k stars) and Nagent (davidondrej/skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Connect Agents?

Mesh-LLM (a GitHub organization) maintains it in Mesh-LLM/mesh-llm, which has 3,485 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 8, 2026.

Source: Mesh-LLM/mesh-llm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Connect Agents

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

connect-agents

Mental model

Built-in launchers (preferred)

Manual config (any OpenAI client)

Validating agent behavior

Blackboard (cross-mesh agent coordination)

Gotchas

Config-only Hermes and OpenClaw

Files

Compare with similar skills

Similar skills

More from Mesh-LLM/mesh-llm

Related

Questions about Connect Agents