| name | higgsfield-assist |
| description | Use 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 get more from fewer credits, or platform efficiency tips.
|
| user-invocable | true |
| metadata | {"tags":["higgsfield","assist","GPT-5","copilot","credits","pricing","optimization","efficiency"],"version":"3.0.0","updated":"2026-04-06T00:00:00.000Z","parent":"higgsfield"} |
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
- Click "Assistant" in the top Higgsfield header
- Select the GPT-5 model
- 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"
- Copy the generated prompt → paste into the relevant feature
When to Use Assist vs Claude with This Skill
| Use Assist for | Use this Claude skill for |
|---|
| Quick prompt generation within the platform | Building complex multi-shot workflows |
| Platform navigation questions | Structuring long-form projects |
| Viral/trend suggestions (platform-current) | Systematic MCSLA prompt construction |
| Real-time platform feature questions | Genre recipe templates and troubleshooting |
| Rapid iteration inside the Higgsfield UI | Understanding the underlying principles |
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
| Plan | Monthly credits | Cost | Best for |
|---|
| Free | 25 | $0 | Testing only |
| Basic | 150 | $6/mo (annual) | Hobby / light use |
| Pro | 700 | $27/mo (annual) | Regular creators |
| Ultimate | 1,500 | $55/mo (annual) | Daily production |
Commercial rights: Basic and above.
Watermarks: Free tier only.
Priority processing: Pro and above.
Credit Cost Tiers (Approximate)
Low cost: Seedance Pro, standard image generation, Nano Banana
Medium cost: Kling 2.6, Wan 2.5/2.6, Minimax Hailuo 2.3, standard I2V
High cost: Sora 2, Kling 3.0 (with audio), Veo 3, Cinema Studio
Apps: Vary widely — one-click apps are generally efficient
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 ../../higgsfield_memory.py ratio <project> --credits
python3 ../../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 Sora 2 / Kling 3.0 for simple shots
Premium models for simple single-character, single-camera shots.
Fix: Reserve premium models for scenes that genuinely need their capabilities.
Kling 2.6 handles most character drama at lower cost.
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 Pro (fast, low cost)
- Character shots: Kling 2.6 only when quality requires it
- Avoid: Sora 2, Kling 3.0, Veo 3
- Strategy: Use Apps heavily — they're credit-efficient for their use cases
Mid budget (Pro plan — 700 credits):
- Primary models: Kling 2.6, Wan 2.5, Minimax Hailuo 2.3
- Reserve Sora 2 / Kling 3.0 for hero shots only
- 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
- Kling 3.0 for anything needing 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 2.6 or Sora 2 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 provideshiggsfield-prompt — MCSLA formula for structured prompt buildinghiggsfield-apps — Apps Assist can recommendhiggsfield-pipeline — Full production workflows
