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-actingWrote 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-acting)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-acting"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-acting/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-acting"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-acting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 89 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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 | $0.00165 | $0.09763 |
| Opus 5.5 | $0.00066 | $0.03905 |
| Sonnet 5.5 | $0.00033 | $0.01953 |
| Haiku 4.5 | $0.00016 | $0.00976 |
Grade A, and why
higgsfield-acting 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 — 636 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Acting Director
[OFFICIAL — Higgsfield "Hell Grind" open-source brief] — the performance system behind
Higgsfield's 95-minute AI feature film, adapted to this repo's prompt surfaces. That tag
covers the untagged text only: § The layer above the pillars, the silent-listener task,
the eye-task paragraph and the voice-bible note each carry their own tag and are not from
the brief (label legend: ../shared/provenance.md).
The core axiom of the entire system: acting is BEHAVIOR under pressure, not a display of emotion. A character wants something, something is in the way, and they act to get it. Emotion is a byproduct of that struggle — never the thing you write directly.
This is a layer on top of ../higgsfield-seedance/SKILL.md: the paragraph this skill
produces goes into the PERFORMANCE / CHARACTER ACTING block of an otherwise normal Seedance
prompt. It does not change camera, light, wardrobe, or grade.
QUICK FACTS
Generated-checked block (scripts/build_index.py verifies anchors). Routing aids — read the linked sections for the rules.
- Write the objective (a verb aimed at a partner), never the state — "make him confess", never "be angry" →
- 2–4 beat changes per scene, each one visible in behavior: a pause, a posture change, a tempo change, a shift of gaze. Unchanged behavior for the whole shot = played flat →
- Listening beats speaking. The reaction starts before the partner's line ends; a hard question earns a micro-pause before the answer →
- The body carries the biography: center of gravity, tempo, openness, breath — set all four before any psychology →
- Business — a physical task the hands are doing — kills theatricality; the strongest accent in a scene is the moment the character stops it →
- Distance is drama; a change of distance is a beat change. Status is what you DO, and status breaks are the most interesting thing in a performance →
- The master profile is 150–220 words, one paragraph, fixed block order, written once per character and then rewritten per scene — never pasted →
- Every tic carries a trigger; every mask carries a crack — at least one "However, when X…" clause per profile →
- Eye life is mandatory and never optional — give the eyes a task aimed at the partner, then saccades, blink quality, eyes-lead-thought; catchlights only make the task legible, they never fix a dead stare. Dead eyes are the number-one tell of AI acting →
- Scene adaptation transforms, never deletes: a behavior that can't physically happen is displaced into another outlet, not removed →
- The voice prompt is locked — one per character, from one voice bible, never adapted per scene; on 2.0 pasted verbatim into the audio field each time they speak, on 2.5 written in the character's role sentence, not the audio field, because the reused sheet carries the voice (once per prompt or once per project is OPEN, no default — P2-2) →
- States, not transitions. Models fail process and nail state: "mid-throw, arm extended", not "reaches in, pulls out, winds up" — for a peak reached through a reversing process; simple same-direction motion that must fill the clip is chained instead →
- Ensemble reactions travel in a wave, never in sync; the strong are still and quiet, the weak fidget and shout →
- 15 named bad-acting symptoms with prompt-level fixes, and a 0–5 self-check scale — aim every hero shot at 4+ →
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 · +39 lines ffa57012eeee
- 1mo ago First seen · 597 lines · 165 tokens per session scan A e76afce5b10f
higgsfield-acting is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (800 stars, last pushed 2d ago), licensed MIT. It adds 165 tokens to every session and 9,763 once invoked, about $0.0007 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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happyhorse-prompter
A tool for writing short prompts for HappyHorse 1.0, an AI video generator that can create video with sound. Its prompts put the main subject first, specify the camera, and describe audible sound when needed.