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 isaacsight/kernel --skill higgsfield-product-photoshootgit clone --depth 1 https://github.com/isaacsight/kernelWrote 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/isaacsight/kernel/higgsfield-product-photoshoot)<a href="https://agentmods.dev/skills/isaacsight/kernel/higgsfield-product-photoshoot"><img src="https://agentmods.dev/badge/skills/isaacsight/kernel/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/isaacsight/kernel/higgsfield-product-photoshoot"><img src="https://agentmods.dev/badge/skills/isaacsight/kernel/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 | $0.00233 |
Grade C, and why
higgsfield-product-photoshoot scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh This is a copy
100% identical to higgsfield-product-photoshoot — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Photoshoot
Brand-image generation via the higgsfield product-photoshoot create command. The CLI calls a backend prompt enhancer that holds mode-specific photography vocabulary and structural templates, then submits to gpt_image_2 and returns image URLs.
Step 0 — Bootstrap
Before any other command:
- If
higgsfieldis not on$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.
- 11d ago First seen · 216 lines · 251 tokens per session scan C 139c38f3b8a3
higgsfield-product-photoshoot is a skill published in the GitHub repository isaacsight/kernel (16 stars, last pushed 12d 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 100% identical to higgsfield-product-photoshoot, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
xiaohongshu-auto-posting
Automates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post…
image-gen
Generate images from text prompts via DashScope/Qwen. Use when creating, drawing, or illustrating images.
whisper
Transcribe audio files to text using OpenAI Whisper.
aov-mingyu-api
An interface to the aov.cc public API for Chinese astrology, divination, tarot, calendars, and related prompt generation.
draw-image
Generate an image from a text prompt using an OpenAI-compatible image generation API (gpt-image-1-mini or compatible). The image is uploaded to the gofile.io public file sharing service and ONLY the public download page URL is returned. Trigger when user asks to draw, paint, generate, or create an image.
meihua-yishu
A Meihua Yishu divination tool, a traditional Chinese method related to the I Ching that interprets hexagrams, or six-line symbols. It can create readings from time, numbers, words or observed surroundings.