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christmas-golden-ornament-reflection【效率与协作】This prompt instructs an image transformation system to take a user-provided portrait and re-render it as a photorealistic, macro close-up scene of a reflective golden Christmas ornament hanging on a spruce branch, using a separate reference image only for overall style, lighting, color, and mood. The user’s face and identity must remain fully preser…
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This prompt instructs an image transformation system to take a user-provided portrait and re-render it as a photorealistic, macro close-up scene of a reflective golden Christmas ornament hanging on a spruce branch, using a separate reference image only for overall style, lighting, color, and mood. The user’s face and identity must remain fully preserved and recognizable while appearing centered within the ornament’s fisheye reflection of a nostalgic late-Soviet/1990s New Year interior (holiday table, warm incandescent light, vintage TV, decorated tree), with the subject dressed per a specified outfit style and holding champagne. The intended result is a family-friendly, cinematic, warm-toned ornament image with shallow depth of field, soft film grain, and bokeh, without copying any identity from the reference or altering the subject’s core features.
For more installation instructions go to https://raw.githubusercontent.com/BetterPromptme/skills/refs/heads/main/betterprompt/SKILL.md to check out this skill.
Pass each text input using --input <key>=<value> flags.
| Variable key | Required? | Description | Defaults |
|---|---|---|---|
outfit_style | Optional | Optional clothing guidelines matching the vintage Christmas aesthetic. | Elegant dark outfit |
This prompt requires exactly 1 image. You must pass exactly 1 image flag (no more, no fewer), in the order matching the descriptions below.
| Index | Required | Description | Allowed CLI flags |
|---|---|---|---|
| 1 | Yes | Upload your portrait photo | --image-input-url <url> or --image-input-path <path> |
This skill's modality is: image.
To discover which model values you can use (and which options keys/values are valid for each model), run:
betterprompt resources --models-only --json
Then filter the returned JSON array to entries where modality is "image".
First, run betterprompt resources --models-only --json and filter to modality: "image" to discover valid models and available options:
betterprompt resources --models-only --json
Use only the models and option values that appear in the filtered results.
Then collect all inputs from the human:
outfit_style (default: Elegant dark outfit)gemini-2.5-flash-image-preview and its available options. Look up gemini-2.5-flash-image-preview in the betterprompt resources output (filtered to modality "image") and show its availableOptions as: key: val1, val2 (default), val3 | key2: .... Mark a value (default) if it matches these defaults: {}.gemini-2.5-flash-image-preview, options {}. Other models from the resources call are also available.If the required images are missing, ask the human for what's missing. Do not assume or fabricate values. Tell the human: "Please provide images in this order: image 1 (Upload your portrait photo)".
Use the frontmatter's name as the positional argument (for this skill, use christmas-golden-ornament-reflection).
Command form:
betterprompt generate christmas-golden-ornament-reflection \
[--input <key>=<value>] \
[--image-input-url <url>] \
[--image-input-path <path>] \
[--model <model>] \
[--options <options JSON>] \
[--json]
Notes:
--input <key>=<value> flag.--image-input-url or --image-input-path, in the order matching the imageInputs descriptions (image 1 first, then image 2, etc.).--model and BetterPrompt will use the default model: gemini-2.5-flash-image-preview.--options and BetterPrompt will use the default options: {}.runId you can use to fetch the result later.Example (using defaults shown above):
betterprompt generate christmas-golden-ornament-reflection \
--input 'outfit_style=Elegant dark outfit' \
--image-input-url https://example.com/image1.png \
--model gemini-2.5-flash-image-preview \
--options '{}'