product-setup

product-setup is a command for Claude Code from lorena-bordonaba-pau/product-studio. It costs 32 tokens per session (580 once invoked), scanned A, original, MIT.

A setup command for choosing and checking the service that creates product images and videos, such as Higgsfield or Fal.ai.

In plain words
What is it for?
Use it when setting up Product Studio for the first time, selecting a provider, validating Fal.ai access, and preparing image or video generation.
Why use it?
It prepares the product tools before use and makes clear whether an existing connection or an API key is needed.

Command for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: reads .claude/ paths; names the AskUserQuestion tool.

Part of the product-studio plugin — 1 skill, 3 commands shipped together

Good fit Use it when setting up Product Studio for the first time, selecting a provider, validating Fal.ai access, and preparing image or video generation.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/lorena-bordonaba-pau/product-studio/product-setup
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.

Clone the repo
git clone --depth 1 https://github.com/lorena-bordonaba-pau/product-studio

Made for: Claude Code.

Or install product-studio, the plugin that ships this one along with the rest of its 1 skill, 3 commands.

Wrote 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.

agentmods badge for product-setup

README.md
[![agentmods](https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-setup.svg)](https://agentmods.dev/commands/lorena-bordonaba-pau/product-studio/product-setup)
Your own site
<a href="https://agentmods.dev/commands/lorena-bordonaba-pau/product-studio/product-setup"><img src="https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-setup.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 580 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00032 $0.00580
Opus 5 $0.00016 $0.00290
Sonnet 5 $0.00006 $0.00116
Haiku 4.5 $0.00003 $0.00058

Measured 7d ago against content hash a70b4b18a51c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

product-setup scanned grade A with 1 finding 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 7d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

`curl -s -o /dev/null -w "%{http_code}" -H "Authorization: Key <KEY>" https://fal.ai/api/models` —
commands/product-setup.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/product-setup — Product Studio initial setup

Guide the user through Product Studio's initial setup. It's a short, friendly process: each team member runs it once. Always reply in the user's language.

Steps

  1. Greet briefly and explain you're going to configure their image/video generation provider.

  2. Detect Higgsfield: check whether the Higgsfield MCP tools (generate_image, generate_video) are available in the session. Mention it when asking ("I can see you have Higgsfield connected" / "I don't see Higgsfield connected in this session").

  3. Ask for the provider with AskUserQuestion:

    • Higgsfield (MCP) — recommended if already connected; no API key needed.
    • Fal.ai — needs an API key (created free at https://fal.ai/dashboard/keys); pay per use.
    • Another tool with an API — for anyone already using a different generation platform.
  4. Depending on the answer:

    • Higgsfield: if not connected, explain how to connect it (Higgsfield MCP connector in Claude's connector settings) and wait until they do.
    • Fal.ai: ask for the API key. Validate it with a cheap test call: curl -s -o /dev/null -w "%{http_code}" -H "Authorization: Key <KEY>" https://fal.ai/api/models — a 401 means the key is invalid; ask again. Don't print the key on screen and don't write it into any project file.
    • Another tool: ask which tool it is, its endpoint, auth type and how it receives a reference image. Save those notes in the config.
  5. Save the configuration to ~/.claude/product-studio/config.json (create the folder if needed):

{
  "provider": "higgsfield | fal | custom",
  "fal_api_key": "...only if applicable...",
  "custom": { "description": "...", "notes": "..." },
  "configured": "2026-07-09"
}
  1. Create the learnings file ~/.claude/product-studio/learnings.md if it doesn't exist:
# Product Studio learnings
Corrections that worked, to be applied automatically in future generations.

Read the full file on GitHub · 53 lines

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. 7d ago First seen · 53 lines · 32 tokens per session scan A a70b4b18a51c

Subscribe to this mod's changes

product-setup is a command published in the GitHub repository lorena-bordonaba-pau/product-studio (6 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 580 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.