Luma Image Generation Skill

Generates images using the Luma AI Photon model (Dream Machine API). Handles API key detection, interactive prompt collection, parameter selection, async polling, and final image download — all via the bundled scripts/luma_imagegen.py CLI.

When to use

• Generate a new image from a text description using Luma AI (Photon / Photon Flash)
• Use a reference image to guide style, structure, or character consistency
• Modify or stylize an existing image using Luma's modify_image_ref

Workflow

1. Check API key — detect LUMA_API_KEY in environment. If missing, guide the user (see below).
2. Collect inputs — ask the user for: prompt, aspect ratio, model choice, and any optional reference images.
3. Build the structured prompt — augment the user's description into a labeled spec (see prompt template below).
4. Run the bundled CLI — execute scripts/luma_imagegen.py with the collected parameters.
5. Poll until complete — the script handles async polling automatically; wait for state: completed.
6. Display result — show the final image URL and download the image to output/luma/.
7. Iterate — if the result doesn't match expectations, adjust the prompt and re-run.

API key detection & setup

Before any API call, check for the key:

python3 ${CLAUDE_SKILL_DIR}/scripts/luma_imagegen.py --check-key

If LUMA_API_KEY is missing:

1. Tell the user the key is not set.
2. Direct them to generate one: https://lumalabs.ai/dream-machine/api/keys
3. Ask them to add it to their .env file or export it in their shell:

   export LUMA_API_KEY=your_key_here

4. Never ask the user to paste the key in chat. Ask them to set it locally and confirm when ready.
5. Once confirmed, retry the --check-key command to verify.

Interactive questions to ask the user

Ask these questions before running the generation:

1. Prompt (required): "What image do you want to generate? Describe the scene, subject, style, and any important details."
2. Aspect ratio (optional, default 16:9): "What aspect ratio? Options: 1:1, 3:4, 4:3, 9:16, 16:9 (default), 9:21, 21:9"
3. Model (optional, default photon-1): "Use photon-1 (higher quality) or photon-flash-1 (faster and cheaper)?"
4. Reference image (optional): "Do you have a reference image URL for style or structure guidance?"

Only ask what's needed — skip questions the user has already answered in their message.

Running the CLI

python3 ${CLAUDE_SKILL_DIR}/scripts/luma_imagegen.py \
  --prompt "YOUR AUGMENTED PROMPT" \
  --aspect-ratio 16:9 \
  --model photon-1 \
  [--image-ref "https://example.com/ref.jpg" --image-ref-weight 0.85] \
  [--out output/luma/]

All flags: | Flag | Default | Description | |------|---------|-------------| | --prompt | (required) | Text description of the image | | --aspect-ratio | 16:9 | 1:1, 3:4, 4:3, 9:16, 16:9, 9:21, 21:9 | | --model | photon-1 | photon-1 or photon-flash-1 | | --image-ref | — | Public URL for style/structure reference | | --image-ref-weight | 0.85 | Weight of reference image (0.0–1.0) | | --modify-ref | — | Base image URL to modify | | --modify-ref-weight | 0.5 | Weight for modification fidelity | | --out | output/luma/ | Output directory for downloaded images | | --poll-interval | 3 | Seconds between polling requests | | --check-key | — | Verify LUMA_API_KEY is set and exit |

Output conventions

• Save final images to output/luma/ with descriptive filenames (e.g., photon1_hero_16x9.png). The output directory is relative to the current working directory when the script is invoked.
• Log the generation ID for reference (useful to retrieve the image later).
• If the generation fails, show the failure_reason from the API response.

Prompt augmentation

Reformat the user's description into a structured spec. Only make implied details explicit — do not invent new requirements.

Template (include only relevant lines):

Primary request: <user's main prompt>
Scene/background: <environment or setting>
Subject: <main subject>
Style/medium: <photo/illustration/3D/cinematic/etc>
Composition/framing: <wide/close-up/overhead; subject placement>
Lighting/mood: <lighting type and emotional tone>
Color palette: <dominant colors or palette notes>
Aspect ratio: <e.g., 16:9 landscape>
Avoid: <elements to exclude>

Augmentation rules:

• Keep it concise — add only what the user implied or provided.
• Always include "Avoid:" to prevent common quality issues (watermarks, logos, blur).
• For modification requests, explicitly list what should change and what must stay the same.

Example augmented prompts

Landscape hero image

Primary request: a misty mountain lake at sunrise
Scene/background: alpine lake surrounded by pine trees, light morning fog
Style/medium: photorealistic nature photography
Composition/framing: wide panoramic, lake centered, mountains in background
Lighting/mood: golden hour, warm and serene
Aspect ratio: 16:9 landscape
Avoid: people, boats, watermarks, oversaturation

Product shot

Primary request: a ceramic coffee mug on a wooden table
Scene/background: warm kitchen interior, soft bokeh background
Subject: minimalist white ceramic mug, steam rising
Style/medium: clean product photography
Lighting/mood: soft diffused window light
Aspect ratio: 1:1 square
Avoid: text, logos, harsh shadows, clutter

Prompting best practices

• Describe scene → subject → style → composition → lighting.
• Mention the intended use (hero image, social post, product shot) to calibrate detail level.
• Use "Avoid:" to eliminate common defects (watermarks, blur, stock-photo clichés).
• For modifications, list invariants explicitly ("change only the background; keep the mug unchanged").
• Start with photon-flash-1 for quick iteration; switch to photon-1 for final quality.
• If the result isn't satisfactory, make one targeted change per iteration.

Models reference

Model	Speed	Quality	Best for
photon-1	Slower	Higher	Final assets, complex scenes
photon-flash-1	Fast	Good	Rapid iteration, drafts

Dependencies

The script uses only the Python standard library. No additional packages are required.