AgentConnect is an open-source platform that lets teams and multiple AI agents collaborate in shared conversations and work processes across services such as Slack, Telegram, Discord, GitHub, GitLab, and Linear. Teams use it to assign agents roles, have agents call one another, and start work from messages, issues, pull requests, webhooks, or schedules.
Borrowing it
Nothing to install: this file belongs to agentconnect-md/agentconnect. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agentconnect-md/agentconnect/main/.claude/skills/agentconnect-setup/SKILL.mdgit clone --depth 1 https://github.com/agentconnect-md/agentconnectWrote 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/agentconnect-md/agentconnect/agentconnect-setup)<a href="https://agentmods.dev/skills/agentconnect-md/agentconnect/agentconnect-setup"><img src="https://agentmods.dev/badge/skills/agentconnect-md/agentconnect/agentconnect-setup/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/agentconnect-md/agentconnect/agentconnect-setup"><img src="https://agentmods.dev/badge/skills/agentconnect-md/agentconnect/agentconnect-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 46 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00092 | $0.01284 |
| Opus 5.5 | $0.00037 | $0.00514 |
| Sonnet 5.5 | $0.00018 | $0.00257 |
| Haiku 4.5 | $0.00009 | $0.00128 |
Grade A, and why
agentconnect-setup scanned grade A with 0 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 2mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set Up AgentConnect
Guide the user one checkpoint at a time. Select one deployment mode, load only its relevant references, verify each checkpoint from evidence, and keep a short progress checklist throughout the tutorial.
Establish the session
- Determine the AgentConnect checkout or deployment directory. Prefer the current directory when it contains
compose.yaml; otherwise ask for or locate the checkout. - Ask whether access is loopback-only, LAN-only, or through stable public domains using Cloudflare Tunnel or another reverse proxy. Never assume
localhost. If anything is externally reachable, read references/network-topology.md and collect the final topology before continuing. - Determine the target mode:
no-auth: loopback-only local evaluation;local-logto: local evaluation with the bundled Logto OSS overlay;cloud-logto: a locally or single-hosted Compose stack using Logto Cloud.
- Determine whether the user wants only the core stack or also Google, GitHub, Slack, a daemon, and production hardening.
- Read references/deployment-modes.md plus references/local-logto.md or references/cloud-logto.md only when that mode is selected. Read references/integrations.md only for requested providers. Read references/operations.md for upgrades, reset, remote-daemon networking, or troubleshooting.
- Inspect the checkout's
compose.yaml, optionalcompose.logto.yaml, and available environment template before issuing commands. If the checkout differs from the bundled references, follow the checkout and current official documentation, and explain the difference.
For an external topology, obtain these values before starting authentication or provider setup:
- the final public Web, API, Relay HTTP, and daemon Relay WebSocket origins;
- the Logto sign-in, admin, issuer, and Management API locations;
- the tunnel/proxy provider, whether its hostname is stable, and its local origin mappings;
- whether daemons run on the Compose host, LAN, or Internet;
- whether an access gateway, WAF, or path rewrite sits in front of any callback or WebSocket endpoint.
What ships with it
8 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.
- 2mo ago First seen · 72 lines · 92 tokens per session scan A 7d87379643f2
agentconnect-setup is a skill published in the GitHub repository agentconnect-md/agentconnect (1,520 stars, last pushed today), licensed Apache-2.0. It adds 92 tokens to every session and 1,284 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
agentconnect-setup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agentconnect-setupagentconnect-md/agentconnect | Skill | 1.5k | today | Apache-2.0 | A | 92 | Claude Code, Codex |
| deploy-genjmagly/aiwg | Skill | 229 | yesterday | MIT | A | 15 | Claude Code |
| deploy-openmaopenma-ai/open-managed-agents | Skill | 344 | today | Apache-2.0 | A | 80 | Claude Code, Codex |
| gearsimota/agent-skills | Skill | 98 | 11d ago | MIT | A | 32 | Claude Code, Codex |
| scaffoldsimota/agent-skills | Skill | 98 | 11d ago | MIT | A | 43 | Claude Code, Codex |
| deploy-mcpfuturesearch/futuresearch-python | Skill | 57 | yesterday | MIT | A | 58 | Claude Code |
| sglang-upgraderedai-infra/Relax | Skill | 580 | 2mo ago | Apache-2.0 | A | 115 | Claude Code, Codex |