Imported from osidemedia/higgsfield-ai-prompt-skill (skills/higgsfield-assist/SKILL.md). Install upstream with npx skills add osidemedia/higgsfield-ai-prompt-skill --skill higgsfield-assist. Copyright stays with the author.

Higgsfield Assist + Credit Optimization

Higgsfield Assist (GPT-5 Powered Copilot)

Location: higgsfield.ai/chat

Higgsfield Assist is a GPT-5 powered creative copilot built directly into the platform. It's separate from Claude — it lives inside Higgsfield's interface and is trained specifically on Higgsfield's tools, workflows, and generation patterns.

What Assist Can Do

• Generate image prompts optimized for the Soul model
• Generate prompts for viral videos in specific styles
• Navigate the platform — recommend which tool to use for a goal
• Recommend the right effects, apps, or presets for your use case
• Answer questions about features and capabilities
• Give feedback on scripts, prompts, or creative concepts
• Suggest fresh ideas when you're blocked
• Help with storyboard planning

How to Use Assist

1. Click "Assistant" in the top Higgsfield header
2. Select the GPT-5 model
3. Ask anything — examples:
   • "Generate a Soul image prompt in the style of a Helmut Newton editorial"
   • "What's the best workflow to create a 30-second branded video with consistent characters?"
   • "Which camera preset works best for a car chase sequence?"
   • "Help me write a prompt for a product video for a skincare brand, sophisticated tone"
4. Copy the generated prompt → paste into the relevant feature

When to Use Assist vs Claude with This Skill

Best workflow: Use this Claude skill to plan and structure → use Higgsfield Assist for final in-platform prompt refinement and quick generation.

Coming Features in Assist

• Generate content (Image, Video, Canvas) directly inside chat
• Upload and analyze media files
• Large file analysis
• Storyboard builder from ideas

Credit Optimization Guide

Understanding Credits

Commercial rights: Basic and above.
Watermarks: Free tier only.
Priority processing: Pro and above.

Plan names, prices, and credit allowances above are hand-maintained and not verifiable from the API catalog (last reviewed 2026-07-06, not re-verified against the live UI) — check higgsfield.ai/pricing before quoting them.

Credit Cost Tiers (Approximate)

Model roster reviewed against the 2026-07-05 catalog snapshot; tier placements are hand-maintained — verify live before quoting.

Low cost: Seedance 2.0 Fast / Mini, standard image generation, Nano Banana 2 Lite Medium cost: Kling 2.6 (legacy), Kling 3.0 Turbo, Wan 2.5/2.6/2.7, Minimax Hailuo 2.3, standard I2V High cost: Kling 3.0 (pro/4K modes), Seedance 2.0 at 1080p/4K, Veo 3 / 3.1, Cinema Studio Apps: Vary widely — one-click apps are generally efficient

"Seedance Pro" is a legacy UI label — not in the API catalog (2026-07-05); its budget slot is now Seedance 2.0 Fast / Mini. Sora 2 is UI-only (confirmed in the UI 2026-07-06) — not in the API catalog as of 2026-07-05; verify in the live UI before recommending.

Quote From the Ledger, Not From Vibes

Before quoting any credit estimate for multi-shot work, run the generation ledger and cite the numbers:

python3 ../../scripts/higgsfield_memory.py ratio <project> --credits
python3 ../../scripts/higgsfield_memory.py budget <project> --shots <manifest.json>

• ratio gives empirical takes-per-kept per shot type, with the structural-vs-stochastic rejection split (high structural% = rewrite the prompt, don't re-roll; high stochastic% = priced re-roll territory).
• budget multiplies a planned shot manifest by those ratios → expected generations + credit estimate with a stated confidence level.
• Never budget from a row marked low-n (under 5 logged generations) — the tool flags them; respect the flag.
• If the ledger is empty or thin, say so explicitly and use the documented default planning ratios — 2–3:1 simple shots, 4–6:1 complex shots — labeled as defaults, not data. The budget command does this labeling automatically; keep the label when you relay the estimate.
• Every logged generation sharpens these numbers — the logging workflow is one command (../higgsfield-recall/SKILL.md § Log the Generation Result).

The 5 Most Common Credit Waste Patterns

1. Generating video before perfecting the image The single biggest waste. If your Hero Frame (base image) isn't right, every animated version will be wrong too. Fix: Spend extra time on image generation (low cost) → animate once (higher cost)

2. Long prompts that fight each other Over-specified prompts create conflicting instructions, forcing multiple regenerations. Fix: Under-specialize on elements you don't care about. Specify only what matters.

3. Changing multiple variables between generations If you change the prompt, the model, AND the camera in one go, you can't learn what fixed what. Fix: Change one thing at a time. Systematic iteration is faster than random retries.

4. Using premium tiers (Kling 3.0 pro/4K, Seedance 2.0 4K, Veo 3.1) for simple shots Premium models for simple single-character, single-camera shots. Fix: Reserve premium models for scenes that genuinely need their capabilities. Kling 3.0 Turbo or Kling 2.6 (legacy) handles most character drama at lower cost — and on Kling 3.0, sound: off gives a silent video at lower credits (per the live spec). Seedance has the same switch (generate_audio: false).

5. Not using Apps for tasks Apps are built for Face swap, product placement, style transfer — doing these manually via prompt takes more credits than the App designed for that task. Fix: Check the Apps library first. If an App covers your use case, use it.

The Hero Frame Efficiency Method

This is the single highest-leverage credit optimization technique:

Step 1: Generate 5–10 image variations (very low credit cost)
         → Find the one that's closest to your vision
Step 2: Refine that one image with inpainting/editing (low cost)
         → Get it exactly right
Step 3: Animate ONCE from the perfect Hero Frame (medium-high cost)
         → First animation attempt is already working with a strong foundation

Result: You spend more on cheap image credits, far less on expensive video credits. The credit math almost always favors this approach.

Model Selection by Budget Scenario

Tight budget (Basic plan — 150 credits):

• Primary model: Seedance 2.0 Fast / Mini (fast, low cost; the old "Seedance Pro" is a legacy UI label — not in the API catalog)
• Character shots: Kling 3.0 Turbo (or legacy Kling 2.6) only when quality requires it
• Avoid: Kling 3.0 pro/4K modes, Seedance 2.0 at 1080p/4K, Veo 3 / 3.1
• Strategy: Use Apps heavily — they're credit-efficient for their use cases

Mid budget (Pro plan — 700 credits):

• Primary models: Kling 3.0 Turbo, Kling 2.6 (legacy), Wan 2.5/2.7, Minimax Hailuo 2.3
• Reserve Kling 3.0 (pro/4K) / Seedance 2.0 4K for hero shots only (Sora 2 is UI-only, confirmed present in the UI 2026-07-06 — not in the API catalog; UI live UI before recommending it)
• Use Cinema Studio for your two or three most important scenes
• Strategy: Iterate in image first, commit in video second

High volume (Ultimate — 1,500 credits):

• Full access to all models
• Cinema Studio as primary workflow for quality content
• Audio is no longer a one-model feature — Seedance 2.0 / 2.0 Mini / 1.5 Pro generate native audio (generate_audio, default on for 2.0), Kling 3.0 and 2.6 have a sound switch, Veo 3.1 Lite has generate_audio. Pick by scene fit, then toggle audio — don't pick the model for the audio.
• Strategy: Invest in Moodboard + Soul ID upfront to avoid style drift

Platform Efficiency Tips

Use presets before writing from scratch Higgsfield's presets (visual styles, motion presets, Cinema Studio genres) encode a lot of quality that's hard to replicate with text alone. Always start with a preset as a base, then customize.

Check the Community gallery before generating Before burning credits on a new style or effect you haven't tried, find a community example that uses it. See what actually works before committing.

Use Assist for quick decisions "Should I use Kling 3.0 or Seedance 2.0 for this?" → ask Assist in 5 seconds rather than generating two test clips.

Save successful prompts When a generation works well, save the complete prompt immediately. Higgsfield doesn't have a native prompt library — you need your own. A simple text file organized by genre works well.

Chain Apps with video for social content Generate a base clip with Kling 2.6, then feed it through an App (Transitions, Style Snap, Urban Cuts) for the final social-ready version. Two steps, total cost is still lower than generating a "perfect" clip from scratch.

Batch similar shots together If you're using the same Soul ID character in 5 different scenes, generate them in the same session. The Hero Frame warm-up time is essentially zero if you're using the same Reference Anchor.

The Platform Learning Path (Credit-Efficient)

Week 1 — Image foundation (Basic plan)

• Master Soul 2.0 + Nano Banana Pro for image generation
• Build your first Soul ID character
• Create a Moodboard for your project
• Target: consistently generating images you're proud of

Week 2 — Simple video (Basic plan)

• I2V with Kling 2.6, single camera control, one scene type
• Target: one good 5-second clip you'd actually use

Week 3 — Cinema Studio (Pro plan)

• One Cinema Studio project, 3–5 shots
• Learn the Hero Frame + Reference Anchor workflow
• Target: a 15–20 second sequence with consistent character

Week 4+ — Full production (Pro or Ultimate)

• Mix Cinema Studio with Apps for efficiency
• Add Moodboard + Soul ID for consistent series content
• Use Higgsfield Assist for rapid iteration

Related skills

• higgsfield-models — Detailed model comparison beyond what Assist provides
• higgsfield-prompt — MCSLA formula for structured prompt building
• higgsfield-apps — Apps Assist can recommend
• higgsfield-pipeline — Full production workflows