What is higgsfield-ai-prompt-skill?

Claude AI skill for cinematic Higgsfield AI prompts — 20 sub-skills covering Cinema Studio 2.5/3.0/3.5, MCSLA formula, Soul ID character consistency, Seedance 2.0 prompt modes, Kling 3.0 Motion Control, Elements system, DISCIPLINE framework, production benchmarks, and 17 templates across 3 categories.. It is open-source on GitHub.

Is higgsfield-ai-prompt-skill free to use?

higgsfield-ai-prompt-skill is open-source under the MIT license, so it is free to use.

What category does higgsfield-ai-prompt-skill belong to?

higgsfield-ai-prompt-skill is listed under skills in the Claudeers registry of Claude-compatible tools.

Higgsfield AI Prompt Skill

A comprehensive Claude skill library for generating high-quality prompts on Higgsfield AI — the cinematic video and image generation platform.

What This Skill Does

Transforms natural language requests into production-ready Higgsfield prompts using:

The MCSLA formula (Model · Camera · Subject · Look · Action)
Named camera controls and motion presets the platform recognizes
Model selection guidance across Kling 3.0 / 3.0 Omni / 3.0 Motion Control, Sora 2, Veo 3.1, Wan, Seedance 2.0, Minimax Hailuo, Higgsfield DoP, and more
Genre recipe templates for action, horror, romance, sci-fi, product ads, and more
Soul ID character consistency guidance + Character Sheet creation
Troubleshooting for failed or poor generations
Cinema Studio 2.5 advanced features: Soul Cast AI actors, built-in color grading, 3D Mode (Gaussian Splatting), Grid Generation, Resolution Settings, Frame Extraction Loop, Object & Person Insertion, Per-Character Emotions, Clustering, Five-View Location Reference Sheet, Reference Sheet Types (Motion / Outfit / Palette), Elements System with library surface (5 source tabs × 6 element categories)
Cinema Studio 3.0 (Business/Team plan): native dual-channel stereo audio, Smart shot control, 15s max duration, 7 genres, @ reference patterns, Soul Cast 3.0
Cinema Studio 3.5: three-pill main UI (Genre / Style / Camera), Style Settings panel (8 Color Palette / 6 Lighting / 9 Camera Moveset Style + Manual Style mode), Camera Settings four-axis panel (3 Camera Body / 5 Lens / 5 Focal Length including new 75mm / 3 Aperture), Image Mode with four Cinematic models picker (Soul Cinema default, Cinematic Characters, Cinematic Locations, Cinematic Cameras with 2.5 vocabulary)
Seedance 2.0 prompting best practices — Intent over Precision, Genre Router, I2V Gate, Anti-Slop, Physics Language, SCELA audio, Reference-Based / Continuation / Expand Shot / Edit Shot / Transformation prompt modes, Continuation Prompt Formula, the Iteration Rule
GPT Image 2.0 prompt director — three-format taxonomy (structured JSON for UI mockups / infographics / reference sheets, dense cinematic prose for single-subject scenes, auto-derive meta-prompt for theme-only concepts) plus reference-sheet and static-ad-recreation workflow satellites
Higgsfield Canvas — node-based / infinite-board workspace guidance: chaining prompts → images → videos, named canvas patterns, Shared Canvas live collaboration, build-free / generate-paid cost model
Marketing Studio + Content Factory — 9 DTC ad presets (UGC / Tutorial / Unboxing / Hyper Motion / TV Spot / Wild Card / Virtual Try-On) with 4–15s ad video, plus an end-to-end campaign pipeline (research → plan → generate → publish → report) with a cost-savings report
Shared negative constraints reference — categorized artifacts + prevention phrases (positive alternatives for 3.0); Kling 3.0 Motion Control failure diagnostic; Physics Rendering — Resolution Decision Matrix (cross-model 480p / 720p / 1080p routing rule for Seedance 2.0 + Cinema Studio 3.x)
Identity vs. Motion separation — hard rule for character consistency across shots
Annotated templates library — 10 genre templates with Cinema Studio 3.0 genre mappings, plus Seedance multi-character coordination + text-overlay sub-libraries (17 files across 3 categories)
DISCIPLINE.md cross-cutting framework — 9 named discipline patterns in 3-3-3 tier symmetry (prompt-construction, model-selection, iteration-discipline) governing decisions across all sub-skills
production-benchmarks.md — Hell Grind 90-min Cannes feature reference, per-character iteration anchors, acceptance-rate calibration; what "production quality" means in practice
FAILURE-MODES.md (Seedance) — 8 named render failures documented with symptom + mechanism + counter for diagnosis-first iteration
C-arc Building Complete AI Projects — 10-Step Methodology — end-to-end pipeline from idea to delivered project; complements the genre/scene templates
Expanded Seedance methodology + Soul refinement — Iteration Rule + 6-Pass Diagnostic Sequence + Four Questions + Next-Shot Decision Tree + Bridging / Continuation / Repair working modes; Character Anchor Block + Two-Tool Refinement Pipeline for character consistency at production scale

Install

Claude Code

git clone https://github.com/OSideMedia/higgsfield-ai-prompt-skill ~/.claude/skills/higgsfield

Claude Cowork

Drop the repo folder into your Cowork workspace. The skill dispatcher is at SKILL.md in the repo root.

Claude.ai Projects

Upload SKILL.md (root) as your project instruction base. Upload files from skills/ as project documents.

Higgsfield Stack Integration

This skill is the prompt-construction layer. Higgsfield ships official execution tooling — a CLI, an MCP custom connector, and a bundled skills package. They complement each other: this skill produces the prompt, their tooling executes it. None of their tooling is required for this skill to work — you can always paste prompts directly into higgsfield.ai. But if you want an end-to-end loop, you'll want one of the three.

A Higgsfield account is required for any of the tooling below. Sign up at higgsfield.ai.

Higgsfield CLI

Command-line tool for terminal-native agents (Claude Code, Codex, Cursor). Per Higgsfield's own guidance, prefer the CLI over the MCP if you're working in a terminal.

Repo: github.com/higgsfield-ai/cli
Install: curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh or brew install higgsfield-ai/tap/higgsfield
Auth: higgsfield auth login

Higgsfield MCP

Custom connector for claude.ai web and the Claude desktop app. Separate product from the CLI.

Connector URL: https://mcp.higgsfield.ai/mcp
Install: claude.ai → Settings → Connectors → Add custom connector → paste the URL above → sign in

Higgsfield Bundled Skills

Markdown skill bundle for agents that consume Cowork-style skills. All three skills drive the CLI under the hood.

Repo: github.com/higgsfield-ai/skills
Install: npx skills add higgsfield-ai/skills
Skills included: higgsfield-generate, higgsfield-soul, higgsfield-product-photoshoot
Invoke: /higgsfield:generate, /higgsfield:soul, /higgsfield:product-photoshoot

End-to-end example

How the layers fit together for a real request:

USER:    "Make me a cinematic chase scene through a night market.
          Use my trained Soul character — reference_id abc123."
   ↓
THIS SKILL — higgsfield-ai-prompt-skill
   • routes to higgsfield-prompt + higgsfield-camera + higgsfield-soul
   • picks Kling 3.0 (character-focused, supports --soul-id)
   • applies MCSLA: model, camera preset, subject, look, action
   • appends shared negative constraints
   • outputs a production-grade Higgsfield prompt
   ↓
PRE-FLIGHT (optional, recommended for Veo / Kling / Sora / Seedance video):

   SCHEMA VERIFY (recommended for any model you haven't called recently):
   CLI path:        higgsfield model get kling3_0
                    → returns schema: aspect_ratio enum, duration range,
                      mode/sound options, media roles
   MCP path:        models_explore(action="get", model_id="kling3_0")
                    → returns same schema as CLI

   COST ESTIMATE (no job submitted):
   MCP path:        generate_video(..., get_cost: true)
                    → returns credit cost + adjustments block

   CLI path:        higgsfield generate cost kling3_0 \
                      --prompt "<prompt from this skill>" \
                      --aspect_ratio 16:9 \
                      --duration 8
                    # (add reference flags as needed: --soul-id, --start-image,
                    #  --end-image — consult `higgsfield model get kling3_0`
                    #  for supported media roles)

   Bundled skills:  drop to CLI for the cost check (same auth, same workspace),
                    then invoke /higgsfield:generate

   Optional account checks (same data across surfaces):
   MCP path:        balance / transactions tools
   CLI path:        higgsfield account status
                    higgsfield account transactions --size 50

   Note: 2.35:1 is anamorphic STYLE vocabulary, not a valid Kling 3.0 output
         ratio. Output ratios are platform-bounded: 16:9 / 9:16 / 1:1 only.
   ↓
HIGGSFIELD STACK — one of three execution surfaces:

   CLI path:
     higgsfield generate create kling3_0 \
       --prompt "<prompt from this skill>" \
       --aspect_ratio 16:9 \
       --duration 8 \
       --wait
     # (add reference flags as needed: --soul-id, --start-image, --end-image —
     #  consult `higgsfield model get kling3_0` for supported media roles)

   Bundled skills path:
     /higgsfield:generate — takes the prompt as its --prompt argument,
     formats the CLI call above under the hood

   MCP path (claude.ai web/desktop):
     Claude invokes the Higgsfield connector with the prompt as input
   ↓
USER:    Result URL returned. Iterate if needed (this skill's
         iteration discipline applies regardless of execution surface).

The layer split holds in every case: this skill always produces the prompt, the Higgsfield stack always handles the generation call. None of the three execution paths reach back into prompt construction; this skill never shells out to their CLI or API.

Full preflight discipline — when to surface it, marketing-studio caveat, CLI naming gotchas (account status, not balance), and the plan-tier-vs-surface framing — lives in skills/higgsfield-stack/SKILL.md § Preflight discipline.

Coexistence rules

For the full coexistence rules, detection signals, naming-collision callouts, and handoff templates, see skills/higgsfield-stack/SKILL.md.

Structure

.
├── SKILL.md                          ← Main dispatcher (routes to sub-skills — start here)
├── README.md                         ← This file
├── CHANGELOG.md                      ← Version history
├── CONTRIBUTING.md                   ← Contribution guidelines
├── LICENSE                           ← MIT license
├── CLAUDE.md                         ← Project instructions for Claude Code
├── .markdownlint.json                ← Linter config (CHANGELOG convention silencing — v3.6.1)
├── model-guide.md                    ← Model comparison tables + decision flowchart
├── image-models.md                   ← Image model reference + pricing tiers
├── vocab.md                          ← Full platform vocabulary reference
├── prompt-examples.md                ← High-quality example prompts + Before/After pairs
├── photodump-presets.md              ← Photodump mode presets
├── DISCIPLINE.md                     ← Cross-cutting discipline framework (9 patterns, 3-3-3 tier symmetry)
├── production-benchmarks.md          ← Production-quality anchors + acceptance-rate calibration
├── higgsfield_memory.py              ← Memory system script
├── seedance_lint.py                  ← Seedance preflight linter
├── validate.py                       ← Pre-release validation script
├── generate_user_guide.py            ← USER-GUIDE.pdf generator (Path B refactor — v3.7.0)
├── validate_user_guide.py            ← USER-GUIDE.pdf drift validator (text-extract + binary diff)
├── db/
│   ├── filter-memory.json            ← Content filter memory (seeded)
│   └── quality-memory.json           ← Quality failure memory (seeded)
├── docs/                             ← Extended reference documents
│   ├── Seedance 2 Skill.md           ← Bilingual EN+ZH Seedance director reference
│   ├── archive/                      ← Historical records
│   │   ├── HISTORY.md                ← Consolidated v3.0.0–v3.6.0 audit + inventory snapshots
│   │   └── AUDIT-2026-06-03.md       ← Full repo audit (security, bugs, docs hygiene)
│   └── user-guide/                   ← Exported USER-GUIDE.pdf + current-version baseline (rotate, not accumulate)
├── templates/                        ← Genre templates + Seedance coordination + text-overlays
│   ├── 01-cinematic-action-chase.md
│   ├── 02-product-ugc-showcase.md
│   ├── 03-horror-atmosphere.md
│   ├── 04-fashion-editorial.md
│   ├── 05-sci-fi-vfx.md
│   ├── 06-portrait-character-intro.md
│   ├── 07-landscape-establishing-shot.md
│   ├── 08-comedy-social-media.md
│   ├── 09-romantic-intimate.md
│   ├── 10-dance-music-performance.md
│   ├── seedance/                     ← Multi-character coordination templates
│   │   ├── multi-character-anchor.md
│   │   ├── single-character-position.md
│   │   ├── top-down-map.md
│   │   └── worked-example-two-character.md
│   └── text-overlays/                ← Text overlay templates
│       ├── slogan.md
│       ├── speech-bubble.md
│       └── subtitle.md
└── skills/
    ├── shared/
    │   └── negative-constraints.md       ← Shared artifact prevention reference
    ├── higgsfield-prompt/SKILL.md        ← Core MCSLA formula + prompt structure + Identity/Motion separation
    ├── higgsfield-image-shots/SKILL.md   ← Cinematic image prompting (shots, angles, composition)
    ├── higgsfield-gpt-image-2/
    │   ├── SKILL.md                      ← GPT Image 2.0 director (JSON / prose / meta-prompt taxonomy)
    │   ├── reference-sheet-workflow.md   ← Automatic product reference-sheet workflow
    │   └── static-ads-workflow.md        ← Static-ad recreation workflow
    ├── higgsfield-models/
    │   ├── SKILL.md                      ← Compact model selection guide
    │   └── MODELS-DEEP-REFERENCE.md      ← Full per-model documentation (on-demand)
    ├── higgsfield-camera/SKILL.md        ← All camera controls + usage
    ├── higgsfield-motion/SKILL.md        ← Named motion presets library
    ├── higgsfield-style/SKILL.md         ← Visual styles + color grades + lighting
    ├── higgsfield-soul/SKILL.md          ← Soul ID character consistency
    ├── higgsfield-audio/SKILL.md         ← Audio prompting + Cinema Studio 3.0 native audio
    ├── higgsfield-apps/SKILL.md          ← One-click Apps guide
    ├── higgsfield-recipes/SKILL.md       ← Genre scene templates
    ├── higgsfield-troubleshoot/SKILL.md  ← Fix failing generations
    ├── higgsfield-assist/SKILL.md        ← General assistant + platform guidance
    ├── higgsfield-mixed-media/SKILL.md   ← Mixed media + hybrid generation
    ├── higgsfield-moodboard/SKILL.md     ← Moodboard creation workflows
    ├── higgsfield-pipeline/SKILL.md      ← Multi-step generation pipelines
    ├── higgsfield-canvas/SKILL.md        ← Node-based Canvas workspace + named patterns + Shared Canvas
    ├── higgsfield-content-factory/
    │   ├── SKILL.md                      ← Campaign pipeline (research → plan → generate → publish → report)
    │   └── publish-and-report-workflow.md ← Publish + cost-savings report satellite
    ├── higgsfield-marketing-studio/
    │   ├── SKILL.md                      ← Marketing Studio: 9 ad presets + 4–15s ad video
    │   └── cross-surface-workflow.md     ← ms_image / DTC Ads cross-surface workflow
    ├── higgsfield-recall/SKILL.md        ← Recall + regeneration patterns
    ├── higgsfield-cinema/SKILL.md        ← Cinema Studio 2.5 + 3.0 + 3.5 (Soul Cast, Color Grading, 3D Mode, Smart Mode, @ References, Native Audio, three-pill UI, Image Mode, Cinematic models picker)
    ├── higgsfield-seedance/
    │   ├── SKILL.md                      ← Seedance prompt director + content-filter preflight
    │   └── FAILURE-MODES.md              ← 8 named Seedance render failures (symptom · mechanism · counter)
    ├── higgsfield-vibe-motion/SKILL.md   ← Vibe-based motion direction
    └── higgsfield-workspaces/SKILL.md    ← Workspace-first decision layer (Cinema Studio / Lipsync / Draw-to-Video / Sora 2 Trends / Click to Ad / Higgsfield Audio)

Generation Ledger

Every generation attempt — kept, rejected, or filter-flagged — gets one row in db/ledger/<project>.json, logged by the agent in ≤5 seconds (one question, one command — never a form). After ~30–40 rows a production has empirical takes-per-kept ratios per shot type instead of vibes:

python3 higgsfield_memory.py log-gen adze --model seedance_2_0 \
  --tags dialogue-cu,two-char --outcome rejected --reason extra-cuts
python3 higgsfield_memory.py ratio adze --credits     # hit rates + money view
python3 higgsfield_memory.py budget adze --shots plan.json   # price before burning

Tags and reject reasons come from controlled vocabularies (db/ledger/README.md); rows are append-only with superseding corrections; ratio splits structural vs stochastic rejections and flags low-n rows instead of faking precision.

Maintenance — two activation steps

Two capabilities ship inactive on purpose and need a one-time setup.

1. Activate the scheduled spec-drift check

.github/workflows/spec-drift.yml runs refresh_specs.py weekly to catch when Higgsfield changes a model's lineup or capabilities before the 30-day staleness warning would. It ships dormant until you give it the Higgsfield CLI credentials as a repo secret:

higgsfield auth login                 # if not already authenticated locally
gh secret set HIGGSFIELD_CREDENTIALS < ~/.config/higgsfield/credentials.json

Then run it once manually (Actions tab → spec-drift → Run workflow) to confirm the CLI-install step resolves on the runner. After that it's automatic: fresh → nothing; drift → it opens/updates a GitHub issue with next steps; auth expired → the job fails so GitHub notifies you to re-run higgsfield auth login and refresh the secret. The credentials live only in the GitHub secret — they are never committed.

2. Let routing telemetry accumulate before pruning

log-route / routing (in higgsfield_memory.py) record which sub-skills each request opens, so "which skills are load-bearing, which to retire" becomes a data question:

python3 higgsfield_memory.py log-route --skills higgsfield-prompt,higgsfield-camera
python3 higgsfield_memory.py routing     # ranks opens, lists the never-opened tail

This is instrumentation, not a verdict — let real requests accumulate before acting on the tail. A small sample is not evidence a skill is dead.

Example Prompts

Basic:

"Write me a Higgsfield prompt for a cinematic action chase through a night market"

Specific:

"I need a horror prompt using VHS style, Dutch angle camera, and the Horror Face preset"

With reference:

"I have a Soul ID character. Write 3 different scene prompts with her — office, party, rooftop"

Model question:

"Should I use Kling 3.0 or Sora 2 for a large-scale battle scene?"

Troubleshoot:

"My image-to-video isn't animating, it's just static. What am I doing wrong?"

The MCSLA Formula

Letter	Layer	Example
M	Model	Kling 3.0
C	Camera	FPV Drone weaving through the alley
S	Subject	A woman in a tactical jacket
L	Look	Cinematic, cold blue shadows, 16:9
A	Action	She sprints, slides under a gate

Built February 2026 · v3.15.2 (updated 2026-06-22) · Platform: higgsfield.ai

Platforms	cli, api, desktop, web, mobile
Operating systems	—
AI compatibility	claude
License	MIT
Pricing	open-source
Language	Python

higgsfield-ai-prompt-skill