Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skillnpx agentmods add skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motionWrote 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/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion/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/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.04616 |
| Opus 5 | $0.00024 | $0.02308 |
| Sonnet 5 | $0.00010 | $0.00923 |
| Haiku 4.5 | $0.00005 | $0.00462 |
Grade A, and why
higgsfield-motion 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 13d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Named Motion Presets
Higgsfield has 100+ named motion presets. Reference them by exact name in your prompt and the platform applies the preset's signature effect. Think of these as VFX macros — one name triggers a complete pre-built visual effect.
Scope caveat
[FIELD — 13-project community harvest + live preset-catalog pull, both 2026-07-18]: motion presets are the viral-effects / social product, not the film grammar. None of the 13 harvested community film/ad productions used a single motion preset — serious film work free-prompts its camera through Cinema Studio / Seedance block briefs (../higgsfield-camera/SKILL.md,../higgsfield-seedance/SKILL.md). Separately, the live catalog pull showed ~100 unique preset names expanded into ~1,900 per-model variants (kling / higgsfield / wan / minimax / seedance families with baked params); category weights at pull time skewed to effects/viral/vfx/ugc. Route preset requests here as ever — but don't steer a filmmaking request into presets when free-prompted camera language is the production-proven path.
How to use in a prompt:
[Your scene description.] Apply the [Preset Name] preset.
Transformation & Body Effects
| Preset | What it does | Best for |
|---|---|---|
| Animalization | Subject transforms into an animal | Werewolf, shapeshifter, fantasy |
| Werewolf | Human-to-werewolf transformation sequence | Horror, fantasy, supernatural |
| Cyborg | Subject gains mechanical/cybernetic components | Sci-fi, body augmentation |
| Turning Metal | Subject or object transforms into metal | Sci-fi, industrial, transformation |
| Disintegration | Subject breaks apart into particles | Dramatic death, magic, sci-fi |
| Gas Transformation | Subject dissolves into gas or smoke | Mystery, supernatural, horror |
| Clone Explosion | Subject multiplies and explodes outward | Action, surreal, energetic |
| Monstrosity | Subject transforms into a monstrous form | Horror, dark fantasy |
| X-Ray | Subject shown with visible internal structure | Medical, sci-fi, surreal |
| Freezing | Subject or scene freezes into ice | Winter, magic, time-stop |
| Tattoo Animation | Tattoos on skin come alive and move | Artistic, body art, fantasy |
| Hair Style | Subject's hair rapidly changes style | Fashion, fun, transformation |
| Luminous Gaze | Eyes emit powerful glowing light | Supernatural, power awakening |
| Black Tears | Subject cries dark/black tears | Emotional horror, dark drama |
| Head Off | Subject's head appears separated from body | Surreal, horror, dark humor |
| Head Explosion | Subject's head explodes dramatically | Action, horror, surreal |
| Duplicate | Subject is duplicated/cloned in frame | Surreal, sci-fi, artistic |
| Ghoulgao | Subject transforms into a ghoul | Halloween, horror |
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.
- 13d ago First seen · 379 lines · 48 tokens per session scan A 32ea4c8982ad
higgsfield-motion is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (531 stars, last pushed 20d ago), licensed MIT. It adds 48 tokens to every session and 4,616 once invoked, about $0.0002 per session on Opus 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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