restaurant-setup

An interactive setup command for connecting a reservation service such as Resy or OpenTable to the tool.

In plain words
What is it for?
Use it to choose a reservation provider, enter credentials, configure an environment variable, and verify the connection with the restaurant doctor command.
Why use it?
It guides you through credential setup without saving your password or access token to a file. You can keep the token in your environment and check whether authentication works.

Command

Part of the restaurant-cli plugin — 1 skill, 5 commands, 3 agents shipped together

Install

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.

agentmods
npx agentmods add commands/omarshahine/restaurant-cli/restaurant-setup
Clone the repo
git clone --depth 1 https://github.com/omarshahine/restaurant-cli

Or install restaurant-cli, the plugin that ships this one along with the rest of its 1 skill, 5 commands, 3 agents.

Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 208 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.00208
Opus 5 $0.00009 $0.00104
Sonnet 5 $0.00003 $0.00042
Haiku 4.5 $0.00002 $0.00021

Measured 3d ago against content hash 070515542940, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

restaurant-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 3d 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.

commands/restaurant-setup.md · 18 lines

What it actually says

Restaurant Setup

Run:

restaurant setup $ARGUMENTS

If no provider id was supplied, ask which provider they want to set up — show the list from restaurant doctor.

The setup flow is fully interactive. For Resy, it prompts for email + password and exchanges them for a durable auth token via POST /3/auth/password. The password is consumed and discarded. It is env-first: the token is NOT written to disk. Setup saves an env tokenRef in config.yaml and prints an export RESY_AUTH_TOKEN='…' line for the user to add to their own environment (e.g. their shell secrets file). The token is read from the environment at runtime.

After the user adds the export line and sources it, run restaurant doctor to confirm auth succeeded. (Until then, doctor reports the token as missing — that's expected.)

Changes

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.

  1. 3d ago First seen · 18 lines · 17 tokens per session scan A 070515542940

Subscribe to this mod's changes

restaurant-setup is a command published in the GitHub repository omarshahine/restaurant-cli (7 stars, last pushed 29d ago), licensed MIT. It adds 17 tokens to every session and 208 once invoked, about $0.0001 per session on Opus 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 2026-08-31.