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 OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-motion-designgit clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skillWrote 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-design)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion-design"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion-design/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-design"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-motion-design.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 | $0.00199 | $0.02329 |
| Opus 5.5 | $0.00080 | $0.00932 |
| Sonnet 5.5 | $0.00040 | $0.00466 |
| Haiku 4.5 | $0.00020 | $0.00233 |
Grade A, and why
higgsfield-motion-design 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 29d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Motion Design
A full motion-design creation flow run through the Higgsfield MCP connector. Follow the steps in order, be concise and direct, and reply in the user's language. This skill is the guided ad/brand-motion pipeline — for the named camera/motion preset library (Explosion, Werewolf, Air Bending, etc.) use higgsfield-motion instead.
Not a spec sheet. Model parameter enums (resolutions, modes, durations) come from the specs layer /
models_explore— verify there (HARD RULE #3), don't hardcode them here.
QUICK FACTS
- Two flows: classicMD (smooth, elegant, cinematic) vs highMD (fast cuts, extreme dynamics, CGI energy) — pick before anything else →
- Ask all brief questions in ONE message — never split intake into rounds →
- Storyboard = one image: a single grid sheet with all N panels via GPT Image 2 — never N separate images →
- Final video = Seedance 2.0 (
seedance_2_0); confirm the model id withmodels_exploreif unsure → - highMD rule: no realistic humans — silhouettes, chrome figures, or 3D abstract shapes only →
- highMD rule: the logo lock is a static hold proportional to clip length (~1s / ~2s / ~2–3s for 5 / 10 / 15s) →
STEP 0 — Determine the flow type
Identify which workflow applies before anything else:
- classicMD — standard ads, brand promos, service presentations, logo reveals, general atmospheric content. Smooth transitions, elegant typography, cinematic feel.
- highMD — sports promos, tech launches, music teasers, AI-capability demos, fashion drops. Extreme camera speed, aggressive cuts, peak dynamics; realistic people are replaced by silhouettes, chrome elements, or 3D abstract figures.
If the request makes the flow obvious, proceed silently. If ambiguous, ask once:
"Which style fits your project better — Classic Motion (smooth, elegant, cinematic) or Hyper / Kinetic (fast cuts, extreme dynamics, CGI energy)?"
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.
- 29d ago Changed 88b05832e4c6
- 1mo ago First seen · 120 lines · 199 tokens per session scan A 7665c169dcae
higgsfield-motion-design is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (800 stars, last pushed 2d ago), licensed MIT. It adds 199 tokens to every session and 2,329 once invoked, about $0.0008 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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