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/dsm5e/aso-trackernpx agentmods add skills/dsm5e/aso-tracker/higgsfield-promptWrote 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/dsm5e/aso-tracker/higgsfield-prompt)<a href="https://agentmods.dev/skills/dsm5e/aso-tracker/higgsfield-prompt"><img src="https://agentmods.dev/badge/skills/dsm5e/aso-tracker/higgsfield-prompt/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/dsm5e/aso-tracker/higgsfield-prompt"><img src="https://agentmods.dev/badge/skills/dsm5e/aso-tracker/higgsfield-prompt.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 | $0.00056 | $0.05660 |
| Opus 5 | $0.00028 | $0.02830 |
| Sonnet 5 | $0.00011 | $0.01132 |
| Haiku 4.5 | $0.00006 | $0.00566 |
Grade A, and why
higgsfield-prompt 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 12d 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 — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Prompt Engineering
The MCSLA Formula
Every high-performing Higgsfield prompt is built on five layers. Think of it as the cinematographer's checklist — fill in each layer and the model has everything it needs.
| Letter | Element | Description | Example |
|---|---|---|---|
| M | Model | Which generation engine | "Use Kling 2.6" |
| C | Camera | Named camera control | "FPV Drone shot weaving through the alley" |
| S | Subject | Who/what + appearance | "A woman in a sand-colored suit, sharp eyes" |
| L | Look | Style + color + lighting | "Cinematic, golden hour, anamorphic flare" |
| A | Action | What happens in the scene | "She turns slowly, wind lifting her coat" |
Prompt Types
Text-to-Video (T2V)
Start from nothing — describe the entire scene from scratch. Best for: establishing scenes, abstract concepts, environments without a specific character.
[Subject + appearance].
[Environment — location, time, weather, atmosphere].
[Action — what happens and how].
[Camera — named control].
[Look — style + color grade].
Example:
A lone astronaut stands on the surface of a red desert planet, helmet visor reflecting
twin moons rising on the horizon. Dust spirals slowly in the thin atmosphere.
She turns to face the camera, gloved hand raised in a slow salute.
Camera: slow Crane Up revealing the vast emptiness behind her.
Style: Cinematic, desaturated orange and deep blue, 2.35:1 anamorphic.
Image-to-Video (I2V)
Animate a provided still image. The image defines the starting frame. Best for: character consistency, product shots, portrait animation, storyboard bring-to-life.
[Reference the input image as the first frame].
[Describe what should move, change, or animate — not what is already visible].
[Camera — named control].
[Style/atmosphere cues].
Example:
Starting from the provided image as the first frame.
The woman's hair lifts gently in the wind. She blinks slowly and turns her gaze
slightly to the left, a faint smile forming.
Camera: subtle Dolly In toward her face.
Style: Cinematic, warm afternoon light, shallow depth of field.
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.
- 12d ago First seen · 506 lines · 56 tokens per session scan A 217a5ae7b0f9
higgsfield-prompt is a skill published in the GitHub repository dsm5e/aso-tracker (139 stars, last pushed 26d ago), licensed MIT. It adds 56 tokens to every session and 5,660 once invoked, about $0.0003 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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