Skill detail

higgsfield

A comprehensive Higgsfield AI prompt skill covering video/image prompt writing, model selection (Kling, Sora 2, Veo, Wan, Seedance, Soul), camera controls, motion presets, Soul ID consistency, and troubleshooting.

Why we recommend it: Dedicated Higgsfield AI prompt skill covering models, camera controls, motion presets, Soul ID, and troubleshooting.

MatchDirectRecommended for Higgsfield
Sourceosidemedia/​higgsfield-ai-prompt-skillExternal source
Reported installs450Popularity signal only

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SKILL.md

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: higgsfield
description: >
  Use this skill whenever the user asks anything about Higgsfield AI — writing or
  refining video/image prompts, choosing a model (Kling, Sora 2, Veo, Wan, Seedance,
  Minimax Hailuo, DoP, Soul, Nano Banana, Seedream, Flux, GPT Image, etc.), camera
  controls, named motion presets, Soul ID character consistency, Cinema Studio 2.5/3.0,
  Vibe Motion, troubleshooting failed generations, credit optimization, Photodump,
  or any mention of higgsfield.ai. Also trigger on generic "write me a video prompt"
  or "make me an AI video prompt" requests when Higgsfield is the user's configured
  platform.
user-invocable: true
metadata:
  tags: [higgsfield, video, image, prompt, cinematic, AI, filmmaking, motion, camera]
  version: 3.35.0
  updated: 2026-08-22
  author: O-Side Media
  license: MIT
---

# Higgsfield AI Prompt Skill

**Language rule:** Reply in whatever language the user writes in.

---

## HARD RULES — pre-delivery checklist

These rules apply to every Higgsfield response. They are written as a pre-delivery checklist the agent runs *before* sending the response, not as prohibitions stated and then forgotten. The failure mode they prevent is **plausibility-over-verification** — producing a response that looks correct because the agent's training data knows the rough shape of Higgsfield work, rather than because the agent actually read the skill files and verified the platform's ground truth.

**Before delivering any Higgsfield response, confirm in this order:**

1. **Routing line present.** First line of response names which sub-skills you routed to (e.g. "Routing to higgsfield-prompt + higgsfield-camera for an Atmosphere push-in"). One line, then the work. Missing routing line = response is incomplete; add it.

2. **Routed sub-skills opened and read in this conversation.** Match the user's ask to the routing table below, open the matching sub-skill files with the read tool, and READ them. Root `SKILL.md` and `skills/higgsfield-prompt/SKILL.md` are mandatory at minimum on any prompt request. Grepped snippets do not satisfy this rule. Full reads do. If your only access to root `SKILL.md` or `skills/higgsfield-prompt/SKILL.md` in this conversation came from grep results, you have not satisfied this rule — open the file. Platform vocabulary, preset names, and model parameters must come from the files because this platform's lineup changes between releases.

3. **Named vocabulary verified, not invented.** Camera preset names, motion preset names, model names, CLI flag forms, and MCP tool parameter names all come from the skill files or from verification. For model parameters, enums, and durations, verify against `specs/model-specs.yaml` first — it is generated from a dated `models_explore` snapshot (see `snapshot_date` inside the file); if the snapshot is stale (>30 days), verify live instead (`higgsfield model get <model>` for CLI param schemas; `models_explore` for MCP). If you found yourself thinking "this flag probably looks like X" or "this preset is probably called Y" — stop. Read the file or run the verification command. Plausibility is not validity. Do not substitute generic video-prompt vocabulary for named Higgsfield presets; do not invent model versions, camera presets, or motion-preset names. If the user names one you don't see in the skill files, say so and ask for clarification.

4. **MCSLA structure intact on video prompts.** Model · Camera · Subject · Look · Action. Five layers, every video prompt, unless the user explicitly opted out.

5. **Shared negative constraints appended.** Pull positive-phrasing prevention phrases from `skills/shared/negative-constraints.md`. Do not paraphrase from training; use the exact phrasing from the file. (Kling 3.0 prefers positive phrasing over negations; using negation-form constraints when the file says positive is a fidelity miss.)

6. **Preflight surfaced when applicable.** If execution intent is signaled (CLI / MCP / bundled-skills mentio
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