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.
A skill your agent uses when the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for…
$ npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-recipes -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-recipes --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-recipes .claude/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .claude/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipesType 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-recipes -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-recipes --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-recipes .agents/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .agents/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipes -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-recipes --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-recipes .cursor/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .cursor/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipes--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-recipes -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OSideMedia/higgsfield-ai-prompt-skill higgsfield-recipes --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-recipes .gemini/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .gemini/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipesInstalls 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-recipes -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-recipes .github/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .github/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipes -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-recipes --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-recipes .opencode/skills/higgsfield-recipes && 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-recipes" agent skill from https://github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skills/higgsfield-recipes into .opencode/skills/higgsfield-recipes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "higgsfield-recipes", 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-recipesA skill your agent uses when the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for…
Higgsfield Recipes is an agent skill from OSideMedia/higgsfield-ai-prompt-skill. Use when the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for prompt examples for a specific style of content.
Its SKILL.md is about 3.2k 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.
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 Recipes loads about 3.2k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 619 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). 619 words, ~3,156 tokens.
.claude/skills/higgsfield-recipes/SKILL.md (or your agent's skills folder).Each recipe is a ready-to-adapt template. Fill in the bracketed fields with your specific details. All examples are compliant — no real names or IPs.
Style: Anamorphicin these recipes is a single-shot Look-line choice — a standalone generation with no location plate, where the words are the only route to the look (rootSKILL.mdHARD RULE 7: a style register, never an output ratio). For a multi-shot Seedance sequence that must hold the lens across shots, the lens goes into the location plates instead and the optics words leave the video prompt (../higgsfield-seedance/SKILL.md§ Bake it into the asset) — one studio's practice; Hell Grind keeps the look in the prompt too, and which holds better is OPEN (../shared/house-rulings.mdP2-6).
Core pattern: Establish → Pursuit → Obstacle → Climax Best models: Kling 2.6 Camera: Action Run, FPV Drone, Crash Zoom In, Bullet Time Style: Cinematic or Anamorphic
Template:
[Subject description — clothing, build, energy] sprints through [environment].
Camera: Action Run — low behind them, matching pace.
[Obstacle appears — what is it]. They [dodge action], barely clearing it.
The camera Whip Pans to [pursuer/threat].
Bullet Time as [climax action — punch, leap, collision].
Style: Cinematic, high contrast, [warm/cold] tones. [Aspect ratio].Example:
A woman in a tactical jacket sprints through a rain-soaked night market,
weaving between stalls and startled vendors.
Camera: Action Run — low behind her, matching her sprint.
A metal gate drops ahead. She slides under it without breaking stride.
Whip Pan to the two men pursuing her through the crowd.
Bullet Time as she leaps from a loading dock onto a moving truck below.
Style: Cinematic, cold blue shadows, amber market light. 16:9.Core pattern: Establish space → Reveal character state → Emotional beat Best models: Kling 2.6, Kling 3.0 Camera: Dolly In, Arc, Head Tracking, Focus Change Style: Cinematic, Super 8MM
Template:
[Character — appearance, posture, state] is in [intimate environment].
[What they are doing — quiet action revealing emotion].
Camera: slow Dolly In toward [their face / hands / significant object].
[Something shifts — they react, realize, remember].
Style: [Cinematic / Super 8MM], [lighting — golden / overcast / practical only].
[Color grade — warm/cool, contrast level].Example:
A grey-haired man sits alone at a kitchen table. An old letter in his hands.
He reads slowly, lips barely moving, eyes growing distant.
Camera: slow Dolly In toward his face.
He looks up at the empty chair across from him.
Style: Cinematic. Warm late-afternoon window light, soft shadows.
Slightly desaturated. 16:9.Core pattern: Texture reveal → Feature showcase → Emotional connection → End frame Best models: Kling 3.0, Nano Banana Pro (for image) Camera: Lazy Susan, Dolly In, Robo Arm Style: Cinematic
Template:
[Product description — no brand name. Color, material, shape, size].
[Setting — surface, environment, lighting setup].
Camera: [Lazy Susan / Robo Arm] revealing [product feature].
[Hero moment — pour, cut, open, glow, activate — macro slow motion].
[Lifestyle context if relevant — who is using it, in what moment].
Style: Commercial ad quality, [clean/warm/dramatic] lighting. [Aspect ratio].
Sound: [product-specific audio cue].Example:
A matte black insulated travel mug, minimal design, no branding.
Placed on a raw concrete countertop beside a morning window.
Camera: Robo Arm arcing slowly from the base up around to the lid.
Hot coffee pours in — steam rises in a slow macro shot.
A hand wraps around the mug. Close-up of warmth on the palms.
Style: Cinematic commercial, warm neutral tones, soft diffused light. 16:9.
Sound: gentle liquid pour, soft ceramic texture.Core pattern: World establish → Reveal tech/threat → Action beat Best models: Kling 2.6, Wan 2.5 (not in the API catalog as of the 2026-09-26 snapshot; may be UI-only — verify in the live UI before recommending) Camera: Crane Up, FPV Drone, Super Dolly Out, Dutch Angle Style: Cinematic or Anamorphic Motion presets: Cyborg, Plasma Explosion, Glitch, Wireframe, Portal
Template:
[Futuristic environment — city, facility, space, wasteland].
[Character — description, what they wear, what they carry].
[The situation — what's happening, what's the threat or goal].
Camera: [movement choice — crane up / FPV / orbit].
[Key visual moment — tech activating, weapon firing, transformation].
Style: [Cinematic / Anamorphic], [cold blue / neon / desaturated orange]. [Ratio].
Apply [motion preset] at [moment in scene].Example:
A battle-worn space station corridor, flickering lights, debris floating in zero gravity.
A soldier in heavy tactical armor drifts along a handrail, rifle raised.
Ahead — a door sealed shut, sparking at the edges.
Camera: FPV Drone drifting ahead of her through the corridor.
She plants an explosive charge. Steps back. Detonation.
Style: Cinematic, cold steel blue, 2.35:1 anamorphic.
Apply Plasma Explosion preset at the moment of detonation.Core pattern: False calm → Wrong detail → Escalation → Reveal Best models: Kling 2.6, Wan 2.5 (not in the API catalog as of the 2026-09-26 snapshot; may be UI-only — verify in the live UI before recommending) Camera: Dolly In, Dutch Angle, Handheld, Crash Zoom In Style: VHS or Cinematic (low key) Motion presets: Horror Face, Raven Transition, Shadow Smoke, Storm Creature
Template:
[Ordinary setting made unsettling — house, street, hospital].
[Character — alone, unaware].
[Wrong detail appears — something that shouldn't be there].
Camera: slow Dolly In toward [the wrong thing].
[Character notices. Reaction.] Camera: Dutch Angle.
Style: [VHS / Cinematic low key], [sickly tones / crushed blacks].
Apply [Horror Face / Raven Transition] preset at the reveal moment.Example:
An empty suburban house at night. Every light on, but no one visible through the windows.
A woman walks up the front path, keys in hand.
She stops. The front door is already open — just an inch.
Camera: slow Dolly In toward the open door crack.
She pushes it open. The hallway is exactly as she left it. Except the mirror at the end
shows a room that doesn't match. Camera: Dutch Angle.
Style: VHS, desaturated greens, practical light only, 4:3.
Apply Horror Face preset in the mirror reflection.Core pattern: Meeting of eyes → Tension → Connection moment Best models: Kling 2.6, Kling 3.0 Camera: Arc, Dolly In, Focus Change, Kiss Style: Cinematic or Super 8MM
Template:
[Two characters — brief appearance note each].
[Setting — intimate, warmly lit, specific].
[The moment — what are they doing, what is the tension].
Camera: [Arc / Dolly In / Kiss control].
[The beat — a look, a touch, a word].
Style: [Cinematic / Super 8MM], [warm golden / soft overcast] light. [Ratio].Example:
Two people stand on a rooftop terrace at dusk, city glowing below them.
They've been talking for hours — coffee cups empty, leaning toward each other.
A long silence. She looks at him.
Camera: Arc slowly around both of them, city blurring behind.
He reaches over and tucks a strand of hair behind her ear.
Style: Cinematic. Golden hour warm tones, shallow depth of field. 16:9.Core pattern: Environment establish → Subject in context → Observational moment Best models: Veo 3, Kling 2.6 Camera: Crane Down, Timelapse Landscape, Dolly Right/Left, Overhead Style: Cinematic (natural grade)
Template:
[Location — specific, vivid, real-feeling].
[Subject — animal, person, phenomenon].
[What is happening — natural behavior, unposed].
Camera: [observational movement — slow pan, timelapse, crane].
[Detail moment — close-up of something specific and visually striking].
Style: Cinematic, natural grade, [time of day], [weather]. [Ratio].
No artificial effects — pure observational documentary feel.Example:
A fog-covered estuary at dawn. Herons standing motionless in the shallows.
One extends its neck. Still. Then strikes — beak in the water, pulls out a small fish.
Camera: Timelapse Landscape as the fog burns off over 10 minutes.
Close-up: water droplets on feathers catching first light.
Style: Cinematic, natural light, neutral grade. 16:9.Core pattern: Establish space → Performance builds → Beat sync climax Best models: Minimax Hailuo 2.3, Kling 2.6 Camera: 360 Orbit, Dolly In/Out on beat, Overhead, Crash Zoom In Style: Cinematic or Anamorphic Motion presets: Glow Trace, Live Concert, Color Rain
Template:
[Dancer/performer — description, outfit].
[Performance space — lighting, scale, atmosphere].
[Dance style / energy — specific movements to describe].
Camera: [movement, ideally synced to beats — orbit, dolly, overhead].
[Climax moment — signature move, camera impact].
Style: [Cinematic / Anamorphic], [color grade — neon / warm / high contrast]. [Ratio].
Apply [Glow Trace / Live Concert] preset for additional visual energy.Example:
A female dancer in a white flowing dress performs alone in a vast black studio.
Only a single overhead spotlight on her.
She moves through contemporary choreography — slow arms, sudden explosive turns.
Camera: 360 Orbit tightening toward her as the movement intensifies.
Overhead shot as she collapses to the floor in the final beat.
Style: Cinematic, pure black and white contrast, 16:9.
Apply Glow Trace preset — her movement leaves a trail of white light.Core pattern: Before state → Trigger → Transformation → After state Best models: Kling 2.6, Wan 2.5 (not in the API catalog as of the 2026-09-26 snapshot; may be UI-only — verify in the live UI before recommending) Camera: Dolly In to trigger, single continuous shot where possible Style: Varies by tone Motion presets: Animalization, Werewolf, Cyborg, Flame On, Turning Metal, Freezing
Template:
[Subject in their before state — normal, restrained, plain].
[Trigger moment — what causes the transformation].
Camera: [movement toward the trigger moment].
[Transformation unfolds — describe the visual change].
[After state — what they become].
Style: [appropriate to transformation type]. [Ratio].
Apply [preset name] preset to execute the transformation effect.Example:
A businesswoman in a grey suit stands in a sterile office, staring out the window.
The lights flicker. She turns. Her eyes begin to glow.
Camera: Crash Zoom In on her eyes.
Her suit tears at the shoulders. Her form expands.
Style: Cinematic, cold fluorescent transitioning to deep red. 16:9.
Apply Monstrosity preset for the transformation sequence.Negative constraints: Before generating from any recipe, check the relevant artifact categories in
../shared/negative-constraints.md. Action recipes → Body/Motion; Horror → Content Filter/Safety; Character-focused → Face/Identity.
Identity vs. Motion: For recipes involving Soul ID characters, split the output into Identity Block + Motion Block. See
higgsfield-promptandhiggsfield-soulfor the rule and examples.
higgsfield-prompt — MCSLA formula, Identity/Motion separationhiggsfield-camera — Camera controls referenced in recipeshiggsfield-motion — Motion presets referenced in recipeshiggsfield-style — Visual styles and color gradeshiggsfield-soul — Soul ID for character-consistent sequenceshiggsfield-models — Model selection per recipe typetemplates/ — Annotated prompt templates expand on these recipes with line-by-line breakdowns© 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-recipes of OSideMedia/higgsfield-ai-prompt-skill.
Open the folder on GitHubat commit 7075497
Higgsfield Recipes 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 Recipes this skillOSideMedia/higgsfield-ai-prompt-skill | 713 | — | ~3.2k | 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
A skill your agent uses when the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for…. Higgsfield Recipes is an agent skill from OSideMedia/higgsfield-ai-prompt-skill.), or asks for prompt examples for a specific style of content.
Higgsfield Recipes fits situations like: the user wants a genre-specific scene template; starting point for a type of video (action; asks for prompt examples for a specific style of content.
Run `npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-recipes -a claude-code`. Or copy the skill folder (skills/higgsfield-recipes in OSideMedia/higgsfield-ai-prompt-skill) into .claude/skills/higgsfield-recipes in your project. Claude Code loads it when a task matches its description.
Run `npx skills add OSideMedia/higgsfield-ai-prompt-skill --skill higgsfield-recipes -a codex`. Or copy the skill folder (skills/higgsfield-recipes in OSideMedia/higgsfield-ai-prompt-skill) into .agents/skills/higgsfield-recipes 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-recipes -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-recipes, .gemini/skills/higgsfield-recipes, .github/skills/higgsfield-recipes and .opencode/skills/higgsfield-recipes in your project.
SKILL.md names no scripts, command-line tools or credentials: Higgsfield Recipes 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 Recipes is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.2k tokens (SKILL.md is roughly 13k 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 Recipes: 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.