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.
git clone --depth 1 https://github.com/lorena-bordonaba-pau/product-studioWrote 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/commands/lorena-bordonaba-pau/product-studio/product-setup)<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>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 | $0.00032 | $0.00580 |
| Opus 5 | $0.00016 | $0.00290 |
| Sonnet 5 | $0.00006 | $0.00116 |
| Haiku 4.5 | $0.00003 | $0.00058 |
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` — 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
-
Greet briefly and explain you're going to configure their image/video generation provider.
-
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"). -
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.
-
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.
-
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"
}
- Create the learnings file
~/.claude/product-studio/learnings.mdif it doesn't exist:
# Product Studio learnings
Corrections that worked, to be applied automatically in future generations.
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.
- 7d ago First seen · 53 lines · 32 tokens per session scan A a70b4b18a51c
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.
Other commands, from other repositories
brand-generate
Generate an on-brand document from a saved Brand Profile.
list
List available Piper TTS TTS voices with optional filtering.
review-video
Make a " Reviews" video — a fast, faceless VO montage of REAL, verified competitor reviews that names the recurring complaints and positions YOUR business as the alternative, then hands off to your own customer testimonials.
transcribe
Transcribe the file at $ARGUMENTS into Markdown using the Frenchie MCP server.
varyshop-carousel
Create a Varyshop multi-slide carousel (LIGHT style, hook → CTA).
imperium:create-video
Create startup video content. Supports pitch videos, product demos, social clips, investor updates, and hiring videos. Bridges to Remotion for rendering.