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-seedance-2-5Wrote 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-seedance-2-5)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-seedance-2-5"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-seedance-2-5/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-seedance-2-5"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-seedance-2-5.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 | $0.00194 | $0.11620 |
| Opus 5.5 | $0.00078 | $0.04648 |
| Sonnet 5.5 | $0.00039 | $0.02324 |
| Haiku 4.5 | $0.00019 | $0.01162 |
Grade A, and why
higgsfield-seedance-2-5 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 1mo 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 — 766 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Seedance 2.5 Director
Seedance 2.5 is a different dialect from Seedance 2.0, not a version bump you can prompt through by habit. 2.0 is a reference-driven shot generator with a 4K lane and a genre hint. 2.5 is an omni-reference production model: up to 50 reference materials, 30-second native runtime, and three non-generation modes — it can edit a video you already have, and extend one forward or backward from its boundary frame.
The prompt grammar changes with it. Reference roles are declared in prose (@Image 1 defines …), audio and text get bracket syntax, long videos are staged with explicit end
states, and first/last frames live inside omni_reference — either as the platform's
start_image / end_image roles or announced inside the prompt — never as a separate
mode.
Model split — read this before writing anything. 2.5 tops out at 1080p (no 4K lane) and has no
genrehint. Since the 2026-09-26 snapshot it does exposestart_image/end_imageroles — but only inomni_referencemode. If the job needs 4K or a genre hint, it is a Seedance 2.0 job —../higgsfield-seedance/SKILL.md. See § Choosing 2.0 vs 2.5.
QUICK FACTS
Generated-checked block (scripts/build_index.py verifies anchors). Routing aids — read the linked sections for the rules themselves.
- Four modes, picked before writing:
t2v·omni_reference·video_edit·video_extension; the mode changes what the prompt is → - Higgsfield surface: 480p/720p/1080p (no 4K), duration 4–30s,
start_image/end_imageonly inomni_reference,t2vtakes zero references, no genre hint,extension_moderequired for (and only for)video_extension→ video_editignoresdurationandaspect_ratioand bills by the source video's length;video_extensioninherits the source's aspect ratio →- Every reference material gets an explicit role and an exclusion — "what to use" plus "what not to use"; never let the model infer the mapping →
- Each material also declares a fidelity grade — full-preserve / partial-preserve / attribute-transfer (name the target) / loose-guide; beat lines name characters (name + one visible marker), never handles →
- Material budget: 30 images / 10 videos ≤30s total / 10 audio ≤30s total, 50 materials max (Dreamina's figures — the platform enforces only the 30-image and 50-material caps) — a platform
start_image/end_imagecounts against both; stability ranges are 1–8 subjects (images), 1–5 subjects at 5–10s (video/audio) → - Multi-reference is a 5-step workflow — map → group → profile → select-by-scene, one line per subject;
@Images 1 through 4 define four charactersis the canonical failure → - Long videos are staged, not paragraphed: one primary change per stage + an explicit end state; timestamps allocate a budget, they are not frame-accurate edit points →
- Staging fixes too many EVENTS; two incompatible JOBS in one generation (physics + performance) is a separate cut — split into two prompts and stitch →
- Bracket syntax:
()music ·<>SFX ·{}dialogue ·【】subtitles; non-Chinese dialogue needs a language line before the line; a music suppression never goes inside()—(no music)there reads as a music cue (house inference, the default) → - First/last frames are
omni_referencework: the platformstart_image/end_imageroles or an in-prompt declaration (@Image 1 is the first frame) — which holds better is unmeasured; keyframes 3+ are always in the prompt; never merge two anchors into one sentence → - Editing needs a sole editing master + edit scope + Timeline Inheritance; extension needs the boundary frame aligned before any new content:
MODE-PLAYBOOKS.md - Storyboard grids, coarse-vs-fine blockouts, one-click video, seamless transitions:
MODE-PLAYBOOKS.md - AI-VFX production pipeline — model-per-asset-class routing, the size-ref frame, location batching, the
omni_referencev2v lane (source ≥4s, duration = source), the four-batch rule, the slop catalog:VFX-PIPELINE.md - Emotion needs 2–4 observable cues, not adjectives; niche camera terms get translated into a visible result →
- The real-person formula is 7 slots — and slot 1 is role, never age: the age-blind engine rule outranks the source guide's
[Age/Race]label → - Hard limits that must not be over-promised (frame accuracy, locked parameters, pixel-identical transitions) →
- Dreamina-product features that are not on the Higgsfield surface — Ultra Long Video 180s, mark-based editing, Clay Renderer →
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +57 lines · +3 tokens per session 6eed25818cff
- 1mo ago First seen · 709 lines · 191 tokens per session scan A 9d61bac33324
higgsfield-seedance-2-5 is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (800 stars, last pushed 2d ago), licensed MIT. It adds 194 tokens to every session and 11,620 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.
Other skills, from other repositories
vibe-creating-prompt
Judges whether a user's input suits the Vibe Creating style of video-prompt writing, and when it does, distills single-scene prompts, multi-shot descriptions, emotional imagery, or mixed input into prompts that are easier for a video model to generate from — while preserving any user-specified dialogue, voiceover…
seedance-cinematic-film
Write photorealistic live-action cinematic Seedance 2.0 prompts for Higgsfield, built on five grounding pillars that stop AI drift and floaty motion. Use for "cinematic film prompt", "shot like a movie", realistic body movement, grounded motion, restrained emotional close-ups, driving scenes, fight choreography…
director-cinematography
Virtual director and cinematographer for AI-generated short videos using Higgsfield. Takes a script, scene, or creative idea and produces a shot-by-shot visual direction plan with ready-to-paste Higgsfield prompts — camera presets, lenses, lighting, color grading, and motion directives for every shot. Channels 11…
seedance-prompt-builder
Write production-ready Seedance 2.0 prompts (image, video, and motion/edit) for Higgsfield. Use this skill whenever the user wants a single Seedance prompt rather than a full shotlist — e.g. "make a Seedance prompt for X", "write me a video prompt", "restyle this clip", "turn my photo into a character sheet"…
kling-prompter
A prompt-writing guide for Kling 3.0, a video-generation model. It covers short videos, story-driven videos with dialogue or sound, and animating an existing reference image.
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.