This prompt instructs the AI to take a user-uploaded photo (optionally matching a specified single-person pose) and transform it into a realistic, festive Christmas portrait where the subject is wrapped like a Christmas tree with snug, depth-aware white crayon ribbon spirals, red/green crayon ornaments, and a yellow crayon star above the head, all rendered with imperfect waxy crayon texture. The result is composited into an outside-at-midnight city street setting and graded to emulate warm Ko...
Scanned 9/12/2026
Install to Claude Code
npx -y skills add BetterPromptme/skills --skill christmas-tree-film-style-photo --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: christmas-tree-film-style-photo
description: This prompt instructs the AI to take a user-uploaded photo (optionally matching a specified single-person pose) and transform it into a realistic, festive Christmas portrait where the subject is wrapped like a Christmas tree with snug, depth-aware white crayon ribbon spirals, red/green crayon ornaments, and a yellow crayon star above the head, all rendered with imperfect waxy crayon texture. The result is composited into an outside-at-midnight city street setting and graded to emulate warm Kodak 200 film shot on a Fuji point-and-shoot, including analog-flash lighting, soft halation, and natural film grain while preserving the person’s facial identity.
compatibility: Requires betterprompt >= 0.1.3
metadata:
openclaw:
homepage: https://github.com/BetterPromptme/skills
emoji: 🧩
requires:
anyBins:
- betterprompt
- bp
os:
- linux
- darwin
install:
- id: node
kind: node
package: betterprompt
bins: [betterprompt, bp]
label: Install BetterPrompt CLI (node)
---
## What it does
This prompt instructs the AI to take a user-uploaded photo (optionally matching a specified single-person pose) and transform it into a realistic, festive Christmas portrait where the subject is wrapped like a Christmas tree with snug, depth-aware white crayon ribbon spirals, red/green crayon ornaments, and a yellow crayon star above the head, all rendered with imperfect waxy crayon texture. The result is composited into an outside-at-midnight city street setting and graded to emulate warm Kodak 200 film shot on a Fuji point-and-shoot, including analog-flash lighting, soft halation, and natural film grain while preserving the person’s facial identity.
> For more installation instructions go to https://raw.githubusercontent.com/BetterPromptme/skills/refs/heads/main/betterprompt/SKILL.md to check out this skill.
## Inputs
### textInputs
Pass each text input using `--input <key>=<value>` flags.
| Variable key | Required? | Description | Defaults |
| --- | ---: | --- | --- |
| `pose` | Optional | Describe the pose you want. | `hands in pockets` |
### imageInputs
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 clear photo. Best results if the photo contains one person only. | `--image-input-url <url>` or `--image-input-path <path>` |
### Models and options
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:
```bash
betterprompt resources --models-only --json
```
Then filter the returned JSON array to entries where `modality` is `"image"`.
## How to run
### Step 1: Collect inputs
First, run `betterprompt resources --models-only --json` and filter to `modality: "image"` to discover valid models and available options:
```bash
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:
- Optional text inputs (use defaults if not provided by the human):
- `pose` (default: `hands in pockets`)
- Required images:
- **Exactly 1** images: image 1 (Upload your clear photo. Best results if the photo contains one person only.). Images must be provided in this order.
- Optional: model and options.
- Present the human with the default model **`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: `{}`.
- If the human does not specify, defaults are used: model `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 clear photo. Best results if the photo contains one person only.)"**.
### Step 2: Run via BetterPrompt CLI
Use the frontmatter's `name` as the positional argument (for this skill, use `christmas-tree-film-style-photo`).
Command form:
```bash
betterprompt generate christmas-tree-film-style-photo \
[--input <key>=<value>] \
[--image-input-url <url>] \
[--image-input-path <path>] \
[--model <model>] \
[--options <options JSON>] \
[--json]
```
Notes:
- Pass each text input as a separate `--input <key>=<value>` flag.
- Pass each image using `--image-input-url` or `--image-input-path`, in the order matching the imageInputs descriptions (image 1 first, then image 2, etc.).
- If the human does **not** mention a model, **omit** `--model` and BetterPrompt will use the default model: **`gemini-2.5-flash-image-preview`**.
- If the human does **not** mention options, **omit** `--options` and BetterPrompt will use the default options: **`{}`**.
- If the run times out, the response will include a `runId` you can use to fetch the result later.
Example (using defaults shown above):
```bash
betterprompt generate christmas-tree-film-style-photo \
--input 'pose=hands in pockets' \
--image-input-url https://example.com/image1.png \
--model gemini-2.5-flash-image-preview \
--options '{}'
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