AI Image

AI Image Creation Workflow

Complete image creation pipeline: classify intent, enhance prompt, generate via fal.ai, recover from errors.

For brand-specific visuals, use a dedicated brand image skill if available. For model selection help, reference /fal-model-selector.

Phase 1: Intent Classification

When a user mentions image creation, FIRST classify their intent into one of six categories:

A. EXPLORING — No clear idea yet

Signals: "help me think of something", "any inspiration", "not sure what to make"

Action:

1. Ask about use case (social media? product? marketing? personal?)
2. Search references/prompt-library-curated.json for relevant examples
3. Show 3-5 options with descriptions
4. Let user pick, THEN proceed to generation

B. BRIEF IDEA — Intent but underspecified

Signals: Description under ~30 words, lacks visual details (composition, lighting, color, texture, perspective) Examples: "portrait photo", "tech logo", "gazebo in garden"

Action:

1. Enhance the prompt using the appropriate style template from references/prompt-templates.md
2. Show the enhanced prompt with brief creative rationale
3. Wait for user confirmation before generating

C. DETAILED PROMPT — User knows what they want

Signals: Multi-sentence prompt with specific visual details, style references, or technical terms Examples: Prompt with lens specs, lighting setup, material descriptions

Action:

1. Generate directly — do NOT over-process
2. Only suggest minor tweaks if obvious improvements spotted

D. EDIT/MODIFY — Change an existing image

Signals: User provides/references an image AND describes a specific change Examples: "add text to this", "change the background", "remove the person"

Action:

1. Keep prompt minimal and edit-focused
2. Upload reference image if local (use fal.ai upload or public URL)
3. Prompt describes ONLY the edit, not the entire image
4. Use edit-capable models: fal-ai/flux-2/edit, fal-ai/gpt-image-1.5/edit, fal-ai/bytedance/seedream/v4.5/edit

E. BATCH REQUEST — Multiple variations

Signals: "4 directions", "multiple versions", "a set of assets"

Action:

1. Plan the variants first, present as a table
2. ASK user which direction(s) to try: "Pick a number, or I can generate all N"
3. NEVER auto-generate all variants without explicit choice
4. Generate AFTER user responds
5. Max 4 parallel per batch

F. CREATIVE + EXTENSIONS — Multi-step workflow

Signals: "design a logo and make mockups", "create X and apply to Y"

Action:

1. Plan 3-5 design directions, present to user, ASK which to try
2. Generate ONLY the chosen direction(s)
3. Show result, get approval
4. THEN plan and generate extensions/derivatives
5. NEVER jump from plan to generating everything

Phase 2: Prompt Enhancement

When the user provides a brief idea (Intent B), enhance using the appropriate style template.

Style Selection

Enhancement Protocol

1. Read the appropriate template from references/prompt-templates.md
2. Apply the template's methodology to transform the user's brief input
3. Output the enhanced prompt in two parts:
   • Narrative Specification: Dense descriptive paragraph
   • Technical Metadata: Visual style, key elements, lighting, composition
4. Show to user, explain creative choices briefly
5. Proceed only after confirmation

Key Principles (from "Blueprint Method")

• Technical precision over feeling: Not "cinematic" but "three-point lighting with warm key, cool fill, 85mm f/1.4"
• Quantifiable spatial logic: Foreground/midground/background, lens specs, lighting setup
• Material physics: How materials interact with light — "brushed aluminum catching overhead softbox", not just "shiny metal"
• Cohesive narrative: Prompt reads like a director's brief, not a tag list

Phase 3: Generation via fal.ai

Default Models by Task

Generation Code Pattern

import fal_client
import os

# Set your FAL API key via environment variable
fal_client.api_key = os.environ.get("FAL_API_KEY")

# Text to image
result = fal_client.subscribe(
    "fal-ai/flux-2-pro",
    arguments={
        "prompt": "YOUR ENHANCED PROMPT HERE",
        "image_size": "landscape_16_9",  # or square, portrait_4_3, etc.
        "num_images": 1,
    }
)
image_url = result["images"][0]["url"]
print(f"Generated: {image_url}")

# Download locally
import urllib.request
urllib.request.urlretrieve(image_url, os.path.expanduser("~/Pictures/ai-image/output.jpg"))

Multi-Direction Parallel Generation

For batch requests (Intent E), generate up to 4 in parallel:

import fal_client
import concurrent.futures
import os

fal_client.api_key = os.environ.get("FAL_API_KEY")

prompts = [
    "Direction 1: ...",
    "Direction 2: ...",
    "Direction 3: ...",
]

def generate(prompt):
    return fal_client.subscribe(
        "fal-ai/flux-2-pro",
        arguments={"prompt": prompt, "image_size": "landscape_16_9"}
    )

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as pool:
    results = list(pool.map(generate, prompts))

for i, r in enumerate(results):
    print(f"Direction {i+1}: {r['images'][0]['url']}")

Image Size Options (fal.ai)

Reference Image Usage

# Edit with reference
result = fal_client.subscribe(
    "fal-ai/flux-2/edit",
    arguments={
        "prompt": "Change the background to a sunset beach",
        "image_url": "https://example.com/original.jpg",
    }
)

# Product photography with reference
result = fal_client.subscribe(
    "fal-ai/image-apps-v2/product-photography",
    arguments={
        "product_image_url": "https://example.com/product.jpg",
        "aspect_ratio": "1:1",
    }
)

Phase 4: Presenting Results

Before generating:

• When enhancing prompts, briefly explain creative direction
• When planning variants, describe each direction distinctively

After generating:

• Show the image URL and local file path
• Do NOT describe or imagine what the image looks like (you cannot see it)
• Keep it brief
• Suggest next steps: "Want to try a different direction?" or "Ready to create extensions?"

Format:

**Direction 1: Modern Minimal**
Image: [url]
Saved to: ~/Pictures/ai-image/...

**Direction 2: Dramatic Lighting**
Image: [url]
Saved to: ~/Pictures/ai-image/...

Phase 5: Error Recovery

When generation fails, diagnose and guide:

Auto-retry logic:

• Network errors: retry once after 5s
• Rate limits: retry once after 30s
• All others: show error + guidance, don't retry

Prompt Library

A curated collection of high-quality prompt templates is available at references/prompt-library-curated.json.

Search it when user is exploring (Intent A) or needs inspiration.

How to search:

import json
import os

with open(os.path.expanduser("~/.claude/skills/ai-image/references/prompt-library-curated.json")) as f:
    library = json.load(f)

# Search by keyword
keyword = "product"
matches = [p for p in library if keyword.lower() in p["prompt"].lower() or keyword.lower() in " ".join(p["categories"]).lower()]

Categories available:

• Product (product photography, packaging, advertising)
• 3D (icons, infographics, typography)
• Photograph (editorial, fashion, landscape)
• Food (food photography, restaurant)
• App (UI/UX mockups)

Decision Tree Summary

User mentions image creation
    |
    v
Classify intent (A-F)
    |
    +-- A (Exploring) --> Search prompt library --> Show options --> User picks --> Enhance --> Generate
    +-- B (Brief idea) --> Enhance prompt --> Show to user --> Confirm --> Generate
    +-- C (Detailed) --> Generate directly
    +-- D (Edit) --> Upload ref image --> Minimal edit prompt --> Generate with edit model
    +-- E (Batch) --> Plan variants --> User picks --> Generate in parallel (max 4)
    +-- F (Multi-step) --> Plan directions --> User picks --> Generate --> Approve --> Extensions
    |
    v
Present results (URL + local path, no description)
    |
    v
Suggest next steps

Integration with Other Skills

Author

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041