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 coleam00/ai-content-factory --skill higgsfieldgit clone --depth 1 https://github.com/coleam00/ai-content-factoryWrote 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/coleam00/ai-content-factory/higgsfield)<a href="https://agentmods.dev/skills/coleam00/ai-content-factory/higgsfield"><img src="https://agentmods.dev/badge/skills/coleam00/ai-content-factory/higgsfield/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/coleam00/ai-content-factory/higgsfield"><img src="https://agentmods.dev/badge/skills/coleam00/ai-content-factory/higgsfield.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 | $0.00073 | $0.00970 |
| Opus 5.5 | $0.00029 | $0.00388 |
| Sonnet 5.5 | $0.00015 | $0.00194 |
| Haiku 4.5 | $0.00007 | $0.00097 |
Grade A, and why
higgsfield 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield CLI
Higgsfield is ONE CLI that drives frontier image + video models from the terminal. It is self-documenting — when you're unsure of a model or a flag, ask the CLI itself:
higgsfield --help # top-level commands (generate, model, voices, upload...)
higgsfield model list # every model (add --video or --image to filter)
higgsfield model get <model> # the EXACT params for a model: duration caps, flags, defaults
higgsfield voices list # TTS voices (for text2speech_v2)
Every generate create job takes --wait (block until done) and prints a result_url — download that URL to get the file. Aliases: higgs, hf. On Windows, if higgsfield isn't on PATH, call %APPDATA%\npm\higgsfield.cmd.
Models you'll actually use
| Job | Model | Notes |
|---|---|---|
| Product image / plate | nano_banana_pro |
~0.15 cr. --image-references <img> to keep/edit a real product (e.g. add branding). |
| Product pan (video, no person) | kling3_0_turbo |
image→video. --start-image <img> --duration 10 --resolution 1080p. |
| UGC talking-head (video + native voice) | gemini_omni |
10s max, generates its own audio + lipsync. --image-references <product.jpg>. |
Make a UGC ad (the main recipe)
One command → a ~10s vertical ad of a person holding a product and reviewing it in their own voice:
higgsfield generate create gemini_omni \
--prompt "A friendly <man|woman> in a bright modern kitchen holding up this <product> toward the camera, casual selfie UGC review, looking right at the camera and speaking warmly in one continuous take: '<~25-word spoken line>' Deliver the line exactly once at a natural, unhurried pace; do NOT repeat, stutter, or loop any word or phrase, and if it finishes before the clip ends, just hold a natural smile in silence. Keep any product logo facing the camera, crisp and legible. Natural handheld vlog style, real skin texture. No on-screen text, no captions, no phone visible in frame." \
--image-references <path/to/branded-product.jpg> \
--duration 10 --aspect_ratio 9:16 --resolution 720p --wait
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.
- 2mo ago First seen · 60 lines · 73 tokens per session scan A 1c4f9a7257a0
higgsfield is a skill published in the GitHub repository coleam00/ai-content-factory (32 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 970 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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