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.
npx skills add S3YED/appie-kit --skill higgsfield-product-photoshootgit clone --depth 1 https://github.com/S3YED/appie-kitWrote 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/s3yed/appie-kit/higgsfield-product-photoshoot)<a href="https://agentmods.dev/skills/s3yed/appie-kit/higgsfield-product-photoshoot"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/higgsfield-product-photoshoot/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/s3yed/appie-kit/higgsfield-product-photoshoot"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/higgsfield-product-photoshoot.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00251 | $0.02332 |
| Opus 5 | $0.00125 | $0.01166 |
| Sonnet 5 | $0.00050 | $0.00466 |
| Haiku 4.5 | $0.00025 | $0.00233 |
Grade C, and why
higgsfield-product-photoshoot scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh This is a copy
95% identical to higgsfield-product-photoshoot — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Photoshoot
Brand-image generation via the higgsfield product-photoshoot create command. The CLI calls a backend prompt enhancer that holds mode-specific photography vocabulary and structural templates, then submits to gpt_image_2 and returns image URLs.
Step 0 — Bootstrap
Before any other command:
- If
higgsfieldis not on$PATH, install it:curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh - If
higgsfield account statusfails withSession expired/Not authenticated, ask the user to runhiggsfield auth login(interactive) and wait for confirmation.
UX Rules
- Be concise. Print only image URLs in the final reply.
- Detect language, respond in it. Mode names and CLI flags stay English.
- Ask at most 4 short questions before submitting. Use labeled options, never open-ended.
- Skip questions whose answer is obvious from context (uploaded image, prior turn, brand memory).
- Never write the gpt_image_2 prompt yourself — backend assembles it.
- Polling is silent. Wait until URLs are ready, then deliver.
Modes
| Mode | When user wants… |
|---|---|
product_shot |
Product on neutral / studio / catalog background |
lifestyle_scene |
Product in real-world environment, hands, action, atmosphere |
closeup_product_with_person |
Tight crop with hands / partial face — beauty application, holding, demonstrating |
moodboard_pin |
Vertical 2:3 Pinterest-native aesthetic, moodboard feel |
hero_banner |
Wide-format website / email / campaign header |
social_carousel |
3–10 connected slides for IG / LinkedIn / Facebook |
ad_creative_pack |
Coordinated pack of static ad variants for Meta / TikTok / Pinterest / Google Ads |
virtual_model_tryout |
Product worn or used by an AI-rendered model |
conceptual_product |
Surreal / CGI-style / levitating / splash / sculptural product |
restyle |
Transform an existing image's aesthetic, mood, or seasonal context |
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.
- 9d ago First seen · 215 lines · 251 tokens per session scan C e1302bff2f80
higgsfield-product-photoshoot is a skill published in the GitHub repository S3YED/appie-kit (9 stars, last pushed 17d ago), licensed MIT. It adds 251 tokens to every session and 2,332 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). It is 95% identical to higgsfield-product-photoshoot, differing in 3 lines, and is treated as a copy.
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