A comprehensive Claude skill library for generating high-quality prompts on Higgsfield AI — the cinematic video and image generation platform.
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
git clone https://github.com/OSideMedia/higgsfield-ai-prompt-skill ~/.claude/skills/higgsfieldDrop the repo folder into your Cowork workspace. The skill dispatcher is at SKILL.md in the repo root.
Upload SKILL.md (root) as your project instruction base. Upload files from skills/ as project documents.
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.
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 | shorbrew install higgsfield-ai/tap/higgsfield - Auth:
higgsfield auth login
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
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
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, notbalance), and the plan-tier-vs-surface framing — lives inskills/higgsfield-stack/SKILL.md§ Preflight discipline.
For the full coexistence rules, detection signals, naming-collision callouts, and handoff templates, see skills/higgsfield-stack/SKILL.md.
.
├── 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
├── scripts/ ← Python tooling (run from the repo root)
│ ├── higgsfield_memory.py ← Memory system script
│ ├── seedance_lint.py ← Seedance preflight linter
│ ├── validate.py ← Pre-release validation script
│ ├── build_index.py ← Regenerates INDEX.md + checks QUICK FACTS anchors
│ ├── sync_specs.py ← Regenerates specs/ from a models_explore snapshot
│ ├── refresh_specs.py ← Spec-drift tripwire (live CLI vs baseline)
│ ├── 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)
│ └── sub_skill_descriptions.py ← Canonical sub-skill roster (shared data module)
├── 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)
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 scripts/higgsfield_memory.py log-gen adze --model seedance_2_0 \
--tags dialogue-cu,two-char --outcome rejected --reason extra-cuts
python3 scripts/higgsfield_memory.py ratio adze --credits # hit rates + money view
python3 scripts/higgsfield_memory.py budget adze --shots plan.json # price before burningTags 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.
Two capabilities ship inactive on purpose and need a one-time setup.
.github/workflows/spec-drift.yml runs scripts/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.jsonThen 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.
log-route / routing (in scripts/higgsfield_memory.py) record which sub-skills each
request opens, so "which skills are load-bearing, which to retire" becomes a
data question:
python3 scripts/higgsfield_memory.py log-route --skills higgsfield-prompt,higgsfield-camera
python3 scripts/higgsfield_memory.py routing # ranks opens, lists the never-opened tailThis is instrumentation, not a verdict — let real requests accumulate before acting on the tail. A small sample is not evidence a skill is dead.
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?"
| 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 · v3.20.1 (updated 2026-07-06) · Platform: higgsfield.ai