Video Generation
bytedance/deer-flow
Generates short videos from a structured JSON prompt, optionally guided by a reference image used as the first or last frame.
Creates and manages reusable character profiles (Soul IDs) for consistent facial and stylistic identity across multiple image and video generations.
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soul --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/higgsfield-soul .claude/skills/higgsfield-soul && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .claude/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soulType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soul --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/higgsfield-soul .agents/skills/higgsfield-soul && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .agents/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soul --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/higgsfield-soul .cursor/skills/higgsfield-soul && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .cursor/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git --path skills/higgsfield-soul--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soul --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/higgsfield-soul .gemini/skills/higgsfield-soul && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .gemini/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soulInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/higgsfield-soul .github/skills/higgsfield-soul && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .github/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-soul --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/higgsfield-soul .opencode/skills/higgsfield-soul && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "higgsfield-soul" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-soul into .opencode/skills/higgsfield-soul/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-soul", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
higgsfield-soulCreates and manages reusable character profiles (Soul IDs) for consistent facial and stylistic identity across multiple image and video generations.
Higgsfield Soul is an agent skill from OSideMedia/higgsfield-ai-prompt-skill. Creates and manages reusable character profiles (Soul IDs) for consistent facial and stylistic identity across multiple image and video generations. Provides identity-vs-motion prompt separation, character sheet creation workflows, micro-expression direction, and Soul Cast AI actor configuration. Use when the user wants to maintain character consistency across multiple generations, asks about Soul ID, creating reusable characters, or generating consistent people across different scenes and shots.
Its SKILL.md is about 14k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Media & Creative, covering AI video generation. The repository describes itself as: Claude AI skill for cinematic Higgsfield AI prompts — 32 sub-skills covering Seedance 2.5 (omni-reference, video edit + extend) and 2.0, the Hell Grind feature-film pipeline, an… The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 7075497. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Higgsfield Soul loads about 14k tokens when it runs. Until then it costs about 129 tokens; SKILL.md has 7,435 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from OSideMedia/higgsfield-ai-prompt-skill at commit 7075497, republished under its MIT licence (© OSideMedia). 7,435 words, ~13,936 tokens.
.claude/skills/higgsfield-soul/SKILL.md (or your agent's skills folder).Generated-checked block (scripts/build_index.py verifies anchors). Read the linked sections for full context — these lines are routing aids, not the rules themselves.
../../templates/ad-asset-prep.md →Soul ID is Higgsfield's character consistency system. Create a character reference once, then reuse it across unlimited generations — different scenes, angles, lighting, and actions — while the face and core appearance remain consistent.
✅ You're building a multi-shot sequence with the same character ✅ You're creating a short film / story with a recurring protagonist ✅ You're making an AI influencer or brand mascot ✅ You want consistent faces across a product ad campaign ✅ You're doing a multi-scene action sequence and need the hero to look the same
❌ Don't use if you only need one shot — overkill for single generations ❌ Don't use if you want maximum creative variation — consistency limits style range
When a Soul ID character is active, your prompt should:
1. Reference the character simply:
The Soul ID character walks through a crowded Tokyo street at night.2. Describe what changes — not what the character looks like:
The Soul ID character now wears a formal black suit. She stands at a podium,
addressing a conference room. Camera: Dolly In toward her face.
Style: Cinematic, cool corporate lighting, 16:9.3. You can change clothing, setting, expression:
The Soul ID character is now in a red dress, dancing alone in a ballroom.
Camera: 360 Orbit.
Style: Cinematic, warm golden chandelier light.Key rule: Don't re-describe the face or core features — the Soul ID handles that. Only describe what is different from the base character.
When Soul ID is active, every prompt MUST be split into two blocks. This is the single most important rule for preventing identity drift.
Contains: face features, clothing, body type, distinguishing marks, color palette. Does NOT contain: any motion, camera, speed, or temporal language.
Contains: camera movement, action choreography, speed, environmental changes. Does NOT contain: any character appearance repetition.
Example 1 — Action scene:
❌ Mixed (bad) — causes identity drift:
A tall woman with green eyes and freckles in a leather jacket sprints through
a warehouse while the camera tracks her and her green eyes flash with determination
and her freckles catch the fluorescent light as she vaults over a railing.Face morphs mid-clip because the model re-reads face descriptors while processing motion.
✅ Separated (good) — identity stays locked:
Identity Block:
The Soul ID character — tall build, green eyes, light freckles across the nose
and cheeks, wearing a fitted black leather jacket, dark jeans.Motion Block:
She sprints through a dimly lit warehouse, vaults over a metal railing
without breaking stride.
Camera: Action Run — low behind her, matching pace.
Fluorescent lights flicker overhead.
Style: Cinematic, cold industrial blue, high contrast. 16:9.Example 2 — Emotional close-up:
❌ Mixed (bad) — face warps during camera move:
A weathered man with deep wrinkles and sad brown eyes wearing a grey wool coat
sits on a park bench as the camera slowly dollies in on his wrinkled face and
sad brown eyes while autumn leaves drift past his grey coat.✅ Separated (good) — face stays sharp:
Identity Block:
The Soul ID character — grey-haired man, deep wrinkles, warm brown eyes,
wearing a heavy grey wool coat, brown leather gloves.Motion Block:
He sits on a park bench, hands folded in his lap, staring at the ground.
A single autumn leaf drifts into frame and lands on the bench beside him.
Camera: slow Dolly In toward his face.
Style: Cinematic. Overcast diffused light, muted earth tones. 16:9.Example 3 — Cinema Studio (@ Elements):
❌ Mixed (bad) — identity in the prompt field:
@Sarah with her dark curly hair and tattoo sleeve walks into the bar.✅ Separated (good) — identity in the Element, motion in the prompt:
@ Element definition (set in Cinema Studio UI):
@Sarah: dark curly hair, tattoo sleeve on left arm, wearing a vintage band tee.Prompt field:
@Sarah pushes open the door and steps inside. She scans the room, then walks
to the far end of the bar. The bartender nods.| Identity Block | Motion Block |
|---|---|
| Face shape, skin tone, eye color | Camera movement name |
| Hair style, color, length | Action verbs (runs, turns, sits) |
| Body type, height, build | Environmental motion (wind, rain, lights) |
| Clothing, accessories, jewelry | Speed and timing cues |
| Scars, tattoos, distinguishing marks | Atmospheric changes (light shifts, fog) |
| Color palette of the character | Style and color grade of the scene |
The quality of your Soul ID reference image determines consistency quality.
For best results:
Image models to generate your Soul ID reference:
[FIELD — Higgsfield Studio, ZEPHYR breakdown, 2026-08-06] The intuitive way to build a
sheet is to put everything on it — every weapon, every state, the mechanism drawn open —
and then reference only the parts you need per shot. That is not how the model reads it.
If it sees a detail, it will try to show it.
The reported failure: a mecha sheet included the cockpit hatch in its open state. The model prioritised the open hatch over the subject's standard look, so most generations came back with the hatch open when it should have been shut — or with the open and closed versions colliding and deforming the whole design. The main body being larger on the sheet did not help; presence beat proportion.
The fix is more sheets, not a richer one:
Captions on a sheet do almost nothing for video. A little label under a detail does not teach the model to operate it — you still have to describe the whole operation in the prompt. The model will not reason "retractable blade, I know how to stage that"; every mechanism gets written out as physical action in the shot that uses it.
This is the sheet-side twin of § Variety Sheets: the question is never "what could this sheet contain" but "what will the model be asked to render from it."
[FIELD — Higgsfield Studio, ONEIRIC + ADILIADA breakdowns, 2026-08-13] A character is
built in two passes with two models, and the discipline is in what happens after:
The two are then assembled into a sheet with one hard condition: the original close-up portrait is never run through a model again. Assembly happens in editing tools around it. Every state change — a scar, a haircut, dirt, a wound, a new wardrobe piece — is integrated point by point with masks, without touching the base.
Why it matters: the base image stays the same pixels, so identity and the skin texture that carries it survive every later version of the character. This is the structural answer to the drift documented in § Two-Tool Refinement Pipeline, where each successive model pass softens texture toward plastic — the base never takes another pass, so it never softens.
How the one-line model fix composes with it (
../shared/house-rulings.mdP2-3). The field fix for a flawed sheet — attach it to Nano Banana 2 with one sentence (../higgsfield-seedance-2-5/VFX-PIPELINE.md§ Stage 1) — is a legal way to make the point edit. On the base, what it returns is a donor: mask the changed region back onto the untouched original, as../higgsfield-seedance/HELL-GRIND.mdalso requires. Whether one full pass alone measurably softens a sheet is unmeasured here; the mask is the default because it costs minutes, while a softened base is paid for in every shot that reads it.
The same rule scales to alternate versions of one character — a different era, a different universe, hero in one world and villain in the next. A new version is a new look, not a new person: wardrobe, makeup, hair, scars and damage all change, and the face stays the same set of pixels. That is what keeps an alternate version still reading as the same person, which is the whole difficulty of the job — living between "make them different" and "make them the same".
A corollary worth stating, because it is where sheets quietly rot: a new state is a new asset with a new name, never an overwrite. A character in the common room and the same character in a hospital bed are two assets of one man.
[EMPIRICAL — Joey character-builder / banana-pro-director 3.0 skills (2026-08-16), re-derived 2026-08-22]
Photoreal character work runs two things that sound like one thing and are not. Separating
them is the single most useful idea in plate building, because "photorealistic" sounds like
it means both and only one of them belongs on a reference.
Axis 1 — biological realism: fully ON. The subject must read as a real living person: pore texture, peach fuzz at jaw and hairline, subsurface scattering, hair strand by strand with flyaways, fabric with real weave and drape, metal with real surface, eyes with depth and moisture. This never comes off.
Axis 2 — photographic capture behaviour: OFF on every plate. A plate is not a photograph of a lit set. No key direction, no shadow side, no cast or contact shadow, no falloff on the background, no spill, no bokeh, no depth-of-field falloff, no vignette, no flare, no haze.
Why Axis 2 is off: these are references, not finished frames. Any lighting baked into a plate — a cheek triangle, a nose shadow, a contact shadow under the feet, a warm bleed on the backdrop — is inherited and amplified by every downstream generation that reads it, and it fights whatever lighting the actual scene wants. The plate carries zero lighting; the scene prompt does all the lighting later.
The trap. Writing "photographed on a real camera by a real photographer on a real set" into a plate prompt switches Axis 2 on along with Axis 1 — and Axis 2 is exactly what poisons a reference. The subject should look like a real person, rendered flat, against nothing.
The one capture phrase that survives: Photographed on a 50mm prime, even sharpness[, soft natural film grain]. Photographed not generated. — the focal length is named in plain words
with no aperture, no bokeh and no falloff attached, so it buys the anti-AI-uniformity signal
without switching on capture behaviour. The bracketed grain clause is an open question, not
part of the settled phrase — see the box below.
The grain clause is contested — OPEN, no default (
../shared/house-rulings.mdP2-4). This source keeps soft natural film grain as part of the anti-uniformity signal.../higgsfield-seedance/HELL-GRIND.md§ The character sheet[OFFICIAL — Hell Grind brief]reports the opposite: bake film grain into the sheet and the character carries that look into every scene and stops reacting to new light. Both failures are inherited by every shot that reads the plate — baked grain cannot be removed per shot; a plate that reads AI-uniform, plastic skin included, softens every shot the same way (§ The Untouched Base guards the same texture) — so neither is clearly the cheaper failure, and nothing here measures it. What both sides share: Axis-1 skin detail stays fully on (Hell Grind's own sheet asks for "real skin with visible pores, no retouch"). Decide per project, pin the choice once, and never vary it across one character's plates.
The distinction most often lost. A photographed seamless is a physical surface — it takes light, it falls off, it catches spill, it holds the subject's shadow, it has a floor the subject stands on. A plate background is none of that. It is a flat uniform colour field with nothing behind the subject at all: no surface, no floor, no wall, no corner, no seam, no horizon, no plane the subject makes contact with. The subject does not stand on anything and does not stand in front of anything, and nothing the subject does affects the field.
Full skin realism is always on — but realism never means unflattering. No acne, blemishes, prominent spots, unrequested scarring, cratered pores, or aggressive detail that reads clinical. The texture is fine, soft, even, natural. Matte is the anti-plastic lever; fine-and-even is the flattering lever, and both run together. Where they conflict, resolve toward flattering — a face should always look good.
This runs counter to instinct. When strong references are attached, the references carry the identity load, and the prompt's job is to say what to DO with that identity in this image. Heavy visual description layered on top of a strong reference creates a double-weight prompt that dilutes the direction the model actually needs from the text.
Rule of thumb: if a sentence re-describes something already visible in an attached reference, cut it unless the composition depends on it. The exception is the first plate — a face lock has no reference to lean on, it is the reference, and it gets the longest, most specific description of anything in this file. Everything after it gets leaner.
When two or more references are attached, say what each one carries so the model does not average them: "face, bone structure and skin tone come from the character reference; wardrobe and accessories come from the look reference."
Lean prompts next to a reference are one side of an open question for video (
../shared/house-rulings.mdP1-1): Higgsfield's feature-film pipeline pastes the full descriptor, word for word, beside the reference (../higgsfield-seedance/HELL-GRIND.md§ The core problem). For image plates this section's rule stands; for a multi-shot video pipeline, read the ruling first.
A character sheet is a multi-angle reference image showing the same character from several viewpoints — typically front, 3/4, side profile, and back. It gives the model comprehensive geometry to work from and dramatically improves consistency.
Two-image floor per character [DEMO — Seedance-4K film tutorial, 2026-07]: never hand a video model a character on a single image. The minimum is one clear face view + one full body — in the tutorial's words, "so Seedance doesn't have to guess." Everything below builds upward from that floor.
Background: put sheets on neutral grey, not white or black. The law is stated once —
the shade range, one pinned hex per project, the three reasons sources give for it, and the
contact-shadow disagreement — in ../../templates/ad-asset-prep.md § Design for win rate.
For a plate that will be read as a reference, the grey is a flat field, not a lit
seamless (§ The Reference Plate — the two axes).
How to create a character sheet:
What to include on a character sheet:
Prompt pattern to generate character sheet content:
Front-facing portrait of [character description], neutral expression, even studio lighting,
clean background. Head and shoulders visible.Then use 3D Mode to orbit and capture additional angles from the same generation.
Why it matters: A multi-angle character sheet gives Soul ID far more geometry data than a single photo. This translates directly into better consistency across extreme angle changes, action shots, and profile views.
Everything above assumes the character is invented. Casting a real person — a client, a friend, the couple in a wedding film — is a different problem, and the sheet pipeline alone does not solve it [DEMO — Higgsfield "AI Love Stories" tutorial, 2026-08]. Feed someone's photos in as references and the sheet comes back as a lookalike actor, not the actual person: costume and body land, the face is a near-miss. It is the kind of near-miss the subject's own family spots instantly and nobody else notices, which makes it the worst kind.
Note what this is NOT. § Face-from-Wide-Shot Workaround below fixes plastic AI faces by cropping a face from a closer generated panel into a wider one — a quality repair, generated pixels throughout. This fixes identity, and the pixels come from a camera.
The hybrid, as demonstrated:
../../templates/ad-asset-prep.md § Design for
win rate).Why it works: the portrait panel is where a video model reads identity, and it is now a real face rather than an approximation of one. The full-body panels still carry wardrobe and proportion, which is all they were ever needed for — and a headless panel cannot contribute a wrong face, so the composite has exactly one face in it and that face is correct.
Constraints, and they are not optional. This puts a real, identifiable person into a generation pipeline. Have their permission for this specific use, keep the source photos out of anything you publish, and do not use the technique on someone who has not agreed to it. The dating, wedding and gift work this method is aimed at is exactly the context where consent is easy to get and easy to skip.
The same sheet prompt through three image models does not produce three qualities of the same thing — it produces three different strengths, and the right answer is per-character [DEMO — Higgsfield "AI Love Stories" tutorial, 2026-08]. In that run, from one prompt across Seedream 5.0 Pro, GPT Image 2 and Nano Banana:
| Model | What it won on, in that comparison |
|---|---|
| Seedream 5.0 Pro | costume texture and wear, and consistency of the costume across all three panels |
| GPT Image 2 | curly hair — full, natural, and identical from every angle; the others could not hold it |
The AI-vs-VFX build routes "clothing, wardrobe changes, branded garments" to GPT Image 2
(../higgsfield-seedance-2-5/VFX-PIPELINE.md § Stage 1); this comparison picked Seedream 5.0
Pro for costume texture on a sheet generated from scratch. The build does not split by job —
reading the two as edits on an existing sheet vs texture from scratch is a [HOUSE]
inference that would let both stand — not a default. Two productions, no measurement here;
OPEN (../shared/house-rulings.md P3-2), and the method below decides per character.
[UNPROVEN HERE] — one production's comparison, on two characters. Treat the method as the finding, not the table: run the same sheet prompt through two or three models and compare before locking, because the axis that decides it is whatever your character is hardest to render (hair, wardrobe wear, skin, a signature prop), and that axis changes per character. Sheets are cheap relative to the shots that depend on them; a wrong lock is paid for in every later generation.
Model specs and current pricing: ../../image-models.md.
An alternative to the multi-step assembly above is generating the entire character sheet in one prompt → one 16:9 image → 3×2 grid with six labeled panels. Same character described once; identity stays maximally locked across all six panels because the generation pass is single. Prefer this for Soul ID reference work when the image model supports a 16:9 grid layout (Nano Banana Pro and similar grid-capable models).
The six panels in canonical order:
Prompt pattern — identity described once at the opening, followed by panel position labels with what's different per panel (stance, framing, focus). Close with: "Identical character identity locked across all six panels. Uniform studio backdrop and lighting across all six panels."
Why single-prompt over multi-step: the multi-step assembly above (Grid Generation + 3D Mode + composite) produces a sheet from multiple independent generations — identity can drift panel-to-panel even with a strong reference. The single-prompt 3×2 grid keeps identity locked because all six panels render together in one pass.
A two-panel sheet pattern for re-dressing an existing character without touching identity [DEMO — Seedance-4K film tutorial, 2026-07] — the tutorial used it to put its lead into a new Y2K outfit:
../../templates/ad-asset-prep.md § Design for win rate.)The split gives the video model one panel to read for what they wear and one for who they are, so the outfit change can't pull the face with it. Registered as a single Element, the sheet carries both. Complements § Multi- Form State Tracking below — a wardrobe state is a state like any other and gets its own sheet.
Where § Character Sheet Creation builds the multi-angle identity reference that goes into Soul ID, the Character Anchor Block is the per-shot prompt structure that locks how that character appears IN a specific shot. The character sheet is build-time; the anchor block is shot-time.
A complete anchor block names, per character in frame, ten attributes:
../higgsfield-seedance/SKILL.md § Frame Coordinate
Systemlooking at Character B)The block sits before the Dynamic Description in the Seedance
output format and feeds a Spatial Layout Block when multiple
characters share frame (see ../higgsfield-seedance/SKILL.md
§ Spatial Layout Block for the multi-character extension).
When a character changes state across the project — wounds from a fight scene that persist into the next scene, a costume change midway through, a transformation across multiple stages — generate a separate anchor sheet per state. Don't rely on the base character sheet plus prompt-text descriptions to track the difference; the model loses the state under iteration pressure.
The discipline is what film production calls script supervising: every shot tracks which version of the character it should match. A character with five stages (initial → fight-injuries → partially- transformed → almost-fully → completely-transformed) gets five distinct Soul ID sheets, one per stage. The shot list references the matching sheet by name; the prompt names the stage in the identity block.
The cost is one character-sheet generation pass per state; the
payoff is that no shot opens a continuity bug that has to be caught
at frame-review time (see ../higgsfield-seedance/FAILURE-MODES.md
§ Frame-level review is mandatory).
In wide shots the face on a character-sheet panel often reads as plasticky — the model deprioritizes facial detail when most of the frame is body or environment. The workaround: render the wide shot, then crop the face from a closer shot (medium-up, shoulders- up, head-and-shoulders) and replace the wide-shot face with the cropped one in post.
This is a character-sheet construction technique, not a generation technique. Keep one closer-shot panel in the character sheet specifically for this purpose — the face you'd cut and paste back into wide shots that need it.
Some props don't generate consistently from a single reference panel — a monster claw the model keeps rendering as a sword, a hand-prop that defaults to a generic shape, an accessory that can't stay continuous across shots. For these, build a separate prop sheet alongside the character sheet:
The inverse pattern to the Embedded prop sheets bullet in § Character Sheet Creation above — embedded works for props that generate cleanly; separate prop sheets are the fallback for props that don't.
For high-investment characters — leads who carry many shots in a project — initial generation in Soul Cinema plus refinement editing in GPT Image 2 produces stronger anchor sheets than either tool alone. Soul Cinema is the Higgsfield first-pass image surface (high-volume batch generation against the character description); GPT Image 2 is a third-party (OpenAI) edit surface that preserves existing image details — particularly facial geometry — when modifying outfits, accessories, lighting, or background elements.
The split is by task:
Scope (../shared/house-rulings.md P2-3). An edit pass is a second full pass. On the
character's identity base — the close-up face plate every shot reads — make the edit, then
mask only the changed region back onto the untouched original (§ Anti-"slop" realism
composite below; § The Untouched Base). A whole-frame edit such as adjust lighting has no
region to mask back, and lighting baked into a reference plate is what § The Reference Plate
keeps off: it belongs on a derived look frame, never on the identity base.
When to reach for both tools: the character will appear in tens of
shots and is worth front-loading iteration cost into. A planning
anchor from a Higgsfield-team production — the lead character of
the 90-minute Cannes feature absorbed ~600 Soul Cinema generations
plus ~200 GPT Image 2 generations before any narrative shot
generation began (see ../../production-benchmarks.md § Per-
character iteration anchor for the full breakdown).
When to stick with one tool: characters appearing in only a handful of shots don't justify the two-tool overhead; a single Soul Cinema pass suffices.
Every GPT Image 2 edit softens Soul Cinema's skin texture toward flat, plastic "AI slop" — the face loses the pore-level realism that made the Soul pass worth doing. A Soul Cinema Re-Pass (re-generate the edited result back through Soul Cinema) is one fix; a layer-mask composite in any photo editor is the faster manual variant, and keeps the original face untouched:
Use the Re-Pass when you want one clean re-generated sheet; use the
layer-mask composite when you must preserve the exact original face and
only graft in the edited region. On a character's identity base only the
composite is legal — the base close-up never takes another pass (§ The
Untouched Base); the Re-Pass is for derived sheets whose own base stays
untouched. Complements the "generate individually +
Photoshop composite" note in ../../image-models.md. Cross-linked from
../../templates/ad-asset-prep.md § Preserve realism after an edit.
Use these facial performance directions to add emotional depth to Soul ID characters. Combine with Soul Cast or character prompting for precise actor-level control.
For muscle-level control — when a named expression below is too coarse and you need the specific facial muscles (a forced vs. genuine smile, a mixed or uncanny expression, AU-per-beat dialogue acting) — see
../higgsfield-facs/SKILL.md(FACS Action Unit codes). Each named expression here decomposes to an AU combination; FACS is the precise-control layer beneath.
| Name | Description | Best for |
|---|---|---|
| Deadpan Neutral | Flat affect, no visible emotion, mask-like stillness | Thriller, interrogation, AI/android characters |
| Fierce Focus | Intense locked gaze, brow slightly lowered, total attention | Action, competition, confrontation |
| Subtle Arrogance | Chin slightly raised, half-lidded eyes, faint smirk | Villain intros, power dynamics, fashion |
| Candid Profile | Unposed side angle, natural and unaware of camera | Documentary, street photography, slice-of-life |
| Post-Workout Fatigue | Heavy lids, parted lips, light sheen of sweat, relaxed muscles | Fitness, aftermath, exhaustion scenes |
| Predator Glare | Unblinking stare, head slightly lowered, eyes locked forward | Horror, thriller, intimidation |
| Sunblind Squint | Eyes narrowed against bright light, slight grimace | Outdoor scenes, desert, beach, golden hour |
| Total Dissociation | Thousand-yard stare, eyes unfocused, emotionally absent | Trauma, shock, psychological drama |
| Controlled Breath | Lips slightly parted, nostrils flared, deliberate calm | Pre-action tension, meditation, recovery |
| Name | Description | Best for |
|---|---|---|
| Suppressed Smile | Fighting back a grin, corner of mouth twitching | Comedy, secret joy, romantic tension |
| Quiet Devastation | Eyes glassy, jaw tight, holding it together | Drama, grief, emotional climax |
| Wary Recognition | Eyes widen slightly, head tilts back a fraction | Reunion, suspicion, plot twist reaction |
| Nervous Composure | Calm face but swallowing, micro-tension in jaw | Interviews, lies, high-stakes poker |
| Cold Calculation | Eyes scanning, no emotional leakage, clinical | Villain strategy, heist planning, espionage |
| Bitter Amusement | One-sided smirk, eyes not smiling | Cynicism, dark humor, betrayal aftermath |
| Exhausted Relief | Eyes closing, shoulders dropping, breath release | Survival, rescue, end of ordeal |
| Frozen Shock | Mouth slightly open, eyes fixed, body still | Jump scares, bad news, sudden revelation |
| Simmering Rage | Clenched jaw, flared nostrils, steady stare | Confrontation, injustice, slow burn tension |
| Vulnerable Openness | Soft eyes, slightly parted lips, unguarded | Romance, confession, emotional honesty |
If you need multiple consistent characters in the same scene:
Shot 1 — establish both:
The Soul ID character [Character A] and a second character [Character B, describe
appearance] face each other across a table. Camera: Arc slowly around them.
Shot 2 — reference both:
The Soul ID character [A] slides a folder across the table.
[Character B] opens it, expression shifting from confusion to realization.
Camera: Dolly In toward [B's] face.Note: Higgsfield can hold multiple Soul IDs. Reference each clearly in the prompt.
Everything above optimizes for one identity locked tight. Point that machinery at a crowd and it backfires: a single-character reference makes a clone army — every soldier, extra, and passer-by renders as the same person [DEMO — Seedance-4K film tutorial, 2026-07]. The tutorial's canonical before/after: an elf army generated from one elf reference came back as identical clones; the fix was a new sheet plus one label change.
The fix — a variety lineup sheet:
@elf — VARIETY reference for the elven army: a sheet of FOUR different elves … the army is a varied host drawn from these four types, every elf unique, no two alike. Reinforce in the positive
locks: "a VARIED host built from the four types in @elf."The role split: 100% matches the reference locks ONE character's
identity; VARIETY reference tells the model to treat the sheet as a
population sample to interpolate a diverse crowd from. Use identity locks
for leads, a variety sheet for the crowd behind them — both can be Elements
in the same prompt. (Reference-role vocabulary:
../higgsfield-seedance/SKILL.md § Reference Roles.)
Pairs with the empty-plate rule for locations — but that rule has a
condition, and it used to be stated here without one. The default still
holds: generate the location with no people and let the video model own the
crowd from the variety sheet (../../templates/ad-asset-prep.md § Location
plates). It gives you one plate that serves every shot, and the crowd stays
directable per shot.
It inverts on a location whose crowd IS the location [DEMO — Higgsfield "AI Love Stories" tutorial, 2026-08]. A night carnival square kept breaking from an empty plate — the model had to invent a packed festival throng from prose on every take, and the takes disagreed with each other. Generating the square already alive with the costumed crowd, baked into the location asset, fixed it, and the reported side effect is the tell: "the prompt stayed clean and simple." The crowd stopped being something every shot prompt had to re-specify.
Which rule applies — decide on the plate, before the shots:
| Empty plate + variety sheet | Crowd baked into the plate |
|---|---|
| the crowd is staffage — passers-by, background traffic | the crowd is the set: a festival, a packed arena, a market at full tilt |
| headcount and behaviour change shot to shot | the same dense mass in every shot of the scene |
| you need the crowd directable ("they scatter") | the crowd is scenery and never takes direction |
| density is low-to-medium | density is so high that prose cannot specify it without bloating every prompt |
The test that decides it: write the crowd out in prose and see how long it is. If specifying it costs a paragraph in every shot prompt, that paragraph belongs in the location asset instead. And verify before committing the scene — that production ran one cheap test generation purely to see whether the baked crowd held, which is the right order.
[UNPROVEN HERE] — one production, one location type. What is certainly true is that the unconditional form of this rule was wrong.
Soul ID is the foundation of Higgsfield's AI Influencer Studio feature.
Workflow:
Prompt pattern for influencer content:
The Soul ID character [name] is in a modern kitchen at golden hour.
She holds a coffee mug, steam rising. She looks directly at camera with a warm smile.
Camera: slight Dolly In. Style: Lifestyle, warm tones, 9:16 vertical.Negative constraints: For face/identity artifacts (face morphing, identity drift, character swap, plastic skin) and their prevention phrases, see
../shared/negative-constraints.md— Face/Identity Artifacts section.
Plan requirement: Cinema Studio 3.0 Soul Cast is available exclusively on Business and Team plans.
Cinema Studio 3.0 carries over Soul Cast's 8 parameter categories from 2.5:
| Category | Options |
|---|---|
| Genre | General, Action, Horror, Comedy, Noir, Drama, Epic |
| Budget | $10M – $500M (affects production value aesthetic) |
| Era | 1900s – 2020s (decade increments) |
| Archetype | Innocent, Everyman, Hero, Caregiver, Explorer, Rebel, Lover, Creator, Jester, Sage, Magician, Ruler (12 options) |
| Identity | Gender, race, age |
| Physical Appearance | Build, height, eye color, hair style, hair texture, hair color, facial hair |
| Details | Scars, tattoos, accessories, distinguishing marks |
| Outfit | Clothing, materials, colors |
| Mode | Purpose | Output |
|---|---|---|
| General | Open-ended character generation | Character image |
| Character | Focused character creation with detailed parameters | Character image |
| Location | Environment/setting generation | Location image |
Use 2–3 clear, well-lit reference shots:
Outfit descriptions must be specific:
fitted olive-green cotton t-shirt, dark indigo slim jeans, white leather sneakers with red accentsIn I2V workflows — describe action, not appearance: The reference image already carries the character's visual identity. Re-describing their appearance creates conflict.
@Image1 — A woman with curly brown hair and green eyes wearing a red jacket walks through the park.@Image1 — She walks through the park, pausing to look up at the falling leaves. Camera: slow tracking alongside.If features drift between shots: Use the character sheet image directly as @Image1 for tighter identity anchoring. A clean, well-lit character sheet outperforms multiple casual photos.
Multi-character scenes: Reference each character separately with distinct @Image tags:
@Image1 as Character A (the detective). @Image2 as Character B (the witness).
Character A leans across the table, speaking firmly. Character B looks away, fidgeting.
Camera: slow push-in on Character B's face.Soul Cinema is the default Cinematic model in the Cinema Studio 3.0 and 3.5 image-mode picker — the model that runs when you toggle Cinema Studio into image mode and do not change the model selection. It is shared across both Cinema Studio versions and is distinct from the older standalone "Soul Cinema Preview" model and from the separately-named Featured-list "Higgsfield Soul Cinema" (see ../higgsfield-cinema/SKILL.md § Image Mode for the disambiguation).
It is a single-step generator. Soul Cinema takes a scene idea ("Describe the scene you imagine") and renders it directly with a cinematic- grade film aesthetic — one prompt → one batch of images, no compositing or multi-pass flow required. The standalone Image-tab controls are: aspect ratio (e.g. 16:9), resolution (e.g. 2k), the enhancer toggle (On/Off), batch size (e.g. 1/4), an optional Color Transfer control (pull a reference image's color/grade onto the generation), and an optional + Character reference (a Soul ID or character image). Cost is roughly 0.125 credits per single image (~0.5 per 4-image batch). The Two-Tool Refinement Pipeline above is an optional second pass through GPT Image 2 when a shot needs a specific edit — not a requirement of Soul Cinema itself, which stands alone as a single-step cinematic scene generator.
That price point is why the Seedance-4K film tutorial reaches for Soul Cinema
"always" for location plates and characters built from scratch — its
on-screen pricing showed 1 credit = 8 images, the cheapest sheet-making pass
on the platform [DEMO — pricing shown on-screen; verify against the current
UI before promising it]. Soul Cinema quality tops out at 2k, so sheets that
must be 4K finish in GPT Image 2 or Nano Banana Pro — the full ladder is in
../../templates/ad-asset-prep.md § Which model makes the sheet.
When a Soul ID is active and Soul Cinema is the selected image model, the same Identity vs. Motion separation rule documented above applies — but Soul Cinema's general-purpose cinematic weighting means the Identity Block does most of the work and the "Motion Block" is replaced by a Scene/Style Block (since image generation has no temporal dimension).
Identity Block — Soul ID reference + static descriptors only:
The Soul ID character — [face/body/wardrobe descriptors only, no camera or motion language].Scene/Style Block — environment + lighting + style direction:
[Setting], [time of day], [lighting quality], [color palette].
Style: Cinematic, [grade], [aspect ratio].Keep the two blocks textually separate in the prompt. Do not re-describe identity inside the Scene/Style block — Soul Cinema is sensitive to identity drift if face/wardrobe descriptors leak into environmental phrasing. For broader picker context (when to pick Soul Cinema vs Cinematic Characters vs Cinematic Locations vs Cinematic Cameras), see ../higgsfield-cinema/SKILL.md § Per-Cinematic-model selection guide.
When a model returns a character that reads as studio-feeling — clean, evenly lit, glossy, slightly plastic — the result is not a final. It's an intermediate. The studio look is what the model defaults to when no atmosphere or grade is doing work in the prompt; it's competently rendered but reads as "AI-generated" rather than "filmed." (Distinct from Cinema Studio, the product — here "studio" describes a visual quality of the output, not the generation environment that produced it.) The fix is to treat the studio-feeling output as a starting frame and re-pass it through Soul Cinema with explicit cinematic direction — palette, grade, lens character, lighting language — until the look lands.
These rules are adapted from the Mr. Core methodology.
Diagnose the studio look. Symptoms: skin reads as smooth/even rather than specular; lighting feels evenly distributed (no key/fill ratio, no directional shadow); palette is wide and accurate rather than graded; clothing fabric reads as new and clean rather than worn or weighted; the frame as a whole looks like a product photograph rather than a film still.
The re-pass workflow. Take the studio-feeling output and:
../higgsfield-cinema/SKILL.md § Style Settings, or
describe one in Manual Style.The studio look is a stop on the path, not the destination. Plan for the re-pass as part of the workflow; don't treat the first generation as the final.
Scope (
../shared/house-rulings.mdP2-3). The re-pass adds exactly what § The Reference Plate switches off — grade, directional light, lens character — so its output is a derived look frame: a final still, or a start frame whose look is the shot. It is never the identity base and never a plate that downstream generations read as a reference. Run it on a copy; the base close-up does not take the pass (§ The Untouched Base).
higgsfield-prompt — MCSLA formula, Identity vs. Motion separation rulehiggsfield-cinema — Cinema Studio Reference Anchor, Soul Cast, @ Elementshiggsfield-moodboard — Soul Hex color palette for character consistencyhiggsfield-pipeline — Multi-shot workflow with Soul IDhiggsfield-recall — Pre-generation memory check for character drift historytemplates/ — Templates 03, 04, 05, 06, 08, 09, 10 include Identity/Motion Block examples© OSideMedia, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/higgsfield-soul of OSideMedia/higgsfield-ai-prompt-skill.
Open the folder on GitHubat commit 7075497
Higgsfield Soul next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Higgsfield Soul this skillOSideMedia/higgsfield-ai-prompt-skill | 713 | — | ~14k | Automated safety check: Pass | MIT | |
| Video Generationbytedance/deer-flow | 84k | 3 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Video Cover Imageitwanger/toBeBetterJavaer | 18k | — | ~3.3k | Automated safety check: Pass | None | |
| Seedancesongguoxs/seedance-prompt-skill | 2.9k | 1 repos | ~2.5k | Automated safety check: Pass | None | |
| HyperFrames Video Entry Pointheygen-com/hyperframes | 60k | 3 repos | ~5.2k | Automated safety check: Pass | Apache-2.0 | |
| Lanshu Create AI Presenter Videocclank/lanshu-create-ai-presenter-video | 2.6k | — | ~3.6k | Automated safety check: Pass | MIT |
bytedance/deer-flow
Generates short videos from a structured JSON prompt, optionally guided by a reference image used as the first or last frame.
itwanger/toBeBetterJavaer
Generate matched 3:4, 16:9, and 4:3 short-video cover images from toBeBetterJavaer video scripts or AI/Java technical topics.
songguoxs/seedance-prompt-skill
This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "即梦", "即梦平台", "视频提示词", "视频生成"…
heygen-com/hyperframes
Entry point for making, editing and rendering videos from HTML compositions with HyperFrames, routing each request to the right workflow.
cclank/lanshu-create-ai-presenter-video
Turn a topic or finished script into a complete, publish-ready explainer video — led by an AI presenter from an authorized adult presenter image, or performed in one of nine visual explainer styles…
eternityspring/reelbench-skills
拉片:把一条成片拆成逐镜头的分析表——每个镜头的时长、景别、类别、运镜、画面. An agent skill from eternityspring/reelbench-skills.
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses whenever the user asks anything about Higgsfield AI — writing or refining video/image prompts, choosing a model (Kling, Veo, Wan, Seedance, Minimax Hailuo, DoP, Soul, Nano…
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses when the user asks about Higgsfield Assist (the built-in GPT-5 copilot), how to use the platform's native AI assistant, credit optimization strategies, plan selection, how to…
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses when the user wants to generate a cinematic still image on Higgsfield, asks about shot framing, camera angle, or composition for image prompts, needs a specific shot type…
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses when the user asks about Mixed Media, wants to apply artistic preset styles to an image (Noir, Sketch, Paper, Canvas, Particles, Neon, etc.), combine multiple artistic…
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses when the user asks about Moodboard, building a moodboard from reference images, curated moodboard presets, Soul Hex color transfer, applying a visual style direction to…
OSideMedia/higgsfield-ai-prompt-skill
A skill your agent uses when the user wants to apply a named Higgsfield motion preset, asks about VFX presets, transformation effects, elemental effects, or transition presets.
Categories
Creates and manages reusable character profiles (Soul IDs) for consistent facial and stylistic identity across multiple image and video generations. Higgsfield Soul is an agent skill from OSideMedia/higgsfield-ai-prompt-skill. Creates and manages reusable character profiles (Soul IDs) for consistent facial and stylistic identity across multiple image and video generations.
Higgsfield Soul fits situations like: the user wants to maintain character consistency across multiple generations; asks about Soul ID; creating reusable characters; generating consistent people across different scenes and shots.
Run `npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a claude-code`. Or copy the skill folder (skills/higgsfield-soul in OSideMedia/higgsfield-ai-prompt-skill) into .claude/skills/higgsfield-soul in your project. Claude Code loads it when a task matches its description.
Run `npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a codex`. Or copy the skill folder (skills/higgsfield-soul in OSideMedia/higgsfield-ai-prompt-skill) into .agents/skills/higgsfield-soul in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-soul -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/higgsfield-soul, .gemini/skills/higgsfield-soul, .github/skills/higgsfield-soul and .opencode/skills/higgsfield-soul in your project.
SKILL.md names no scripts, command-line tools or credentials: Higgsfield Soul is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Higgsfield Soul is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 14k tokens (SKILL.md is roughly 56k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Higgsfield Soul: Video Generation (bytedance/deer-flow, 84k stars), Video Cover Image (itwanger/toBeBetterJavaer, 18k stars), Seedance (songguoxs/seedance-prompt-skill, 2.9k stars) and HyperFrames Video Entry Point (heygen-com/hyperframes, 60k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
OSideMedia (a GitHub user) maintains it in OSideMedia/higgsfield-ai-prompt-skill, which has 713 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on September 27, 2026.
Source: OSideMedia/higgsfield-ai-prompt-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.