higgsfield-assist

# 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

| 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:**

```bash
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 c