Muapi Chibi Collage Effect Agent Skill
Turn a real lifestyle photo into a polished "chibi clone sticker diary" image — the original person stays photorealistic, surrounded by 5–8 kawaii chibi mini-clones, scrapbook doodles, and handwritten-style captions that match the scene.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/SamurAIGPT/Generative-Media-Skills --skill muapi-chibi-collage-effectThe instruction itself
6 sections, as written by the authorChibi Collage Effect
Turn a real lifestyle photo into a polished "chibi clone sticker diary" image — the original person stays photorealistic, surrounded by 5–8 kawaii chibi mini-clones, scrapbook doodles, and handwritten-style captions that match the scene.
Inputs
| Name | Type | Required | Default | Description |
|:---|:---|:---|:---|:---|
| person_image | image_url | yes | — | A clear lifestyle photo of the person. Setting, outfit, mood, and activity are read from this image and drive the chibi poses and captions. |
Steps
Phase A — Chibi Collage Generation
If {{person_image}} is not provided, ask the user to upload a lifestyle photo (café, outdoor, cozy at home, travel, etc.). The scene context is what makes the chibi clones feel native to the moment, so a generic studio headshot will give weaker results than a real lifestyle shot.
Once the photo is available, submit the plan with ONE step to generate the chibi collage:
- Chibi Collage Generation —
muapi image edit(model=gpt-image-2-image-to-image): - Reference Image:
{{person_image}} - Image size:
2160x3840(9:16 portrait) — high-resolution social-media-ready output - Background:
auto - Output format:
png - Quality:
auto - Moderation:
low - Prompt:
Create a high-quality "chibi clone sticker diary photo" based on the uploaded real-life image. Preserve the original person's identity, face, hairstyle, hair color, outfit, body proportions, pose, lighting, and background. Do not alter facial features or turn the subject into a full illustration—maintain a realistic photo look.
Analyze the uploaded image carefully: identify the setting, mood, activity, clothing style, and overall vibe of the scene. Use this analysis to determine the theme of the chibi stickers, poses, emotions, and text phrases — everything should feel native to the actual moment captured in the photo.
Add 5–8 chibi mini clones of the same person around the subject, designed in a consistent kawaii sticker style (big head, small body, large expressive eyes, clean digital finish). Each clone must clearly resemble the real person (same hair, outfit, colors).
Design each chibi with different actions and emotions that are directly relevant to what the person is doing or feeling in the photo — inferred naturally from the scene (e.g. if they're at a café: sipping coffee, reading, daydreaming, chatting; if outdoors: exploring, laughing, taking photos; if cozy at home: cuddling, reading, relaxing). Ensure all poses are unique and contextually matched to the image.
Render each chibi as a sticker with white outlines, soft shadows, and a slightly floating effect. Arrange them around the subject and edges without covering the face or main body.
Add light hand-drawn doodles (hearts, sparkles, arrows, motion lines, circles, stars) in white with subtle pink accents, keeping a clean scrapbook diary feel. Doodle style should complement the mood of the scene.
Include 5–8 short handwritten-style phrases that match the mood and context of the uploaded photo — cute, expressive, and scene-appropriate. Use mostly white text with slight pink highlights and small decorative marks.
Composition: keep the real person as the central focus, surrounded by chibi stickers and doodles. The result should feel like a polished, playful, high-resolution social media lifestyle diary image — clean, balanced, and visually rich without clutter.
Present the generated chibi collage to the user. Suggest variations they can try: different source photos (travel, café, cozy-at-home), or asking to bias the captions toward a specific tone (cute, sassy, journal-style).
Trigger Keywords
chibi collage, chibi sticker diary, mini me stickers, kawaii clone collage, sticker diary photo, scrapbook chibi
Notes for the Executing Agent
- This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call
muapiCLI commands. Usemuapi auth configurefirst ifMUAPI_API_KEYis unset. - For model IDs without a CLI alias yet, fall back to the raw endpoint via
curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}'and poll withmuapi predict wait <request_id>. - Substitute
{{input_name}}placeholders with the user's actual inputs before issuing each call. - Source schema reference:
gpt-image-v2-editmaps togpt-image-2-image-to-imagein the muapi catalog. - The output is intentionally 9:16 (2160×3840) so it's ready for IG Stories / Reels / TikTok / YT Shorts without re-cropping.
Other skills for the same job
different authors, same section of the catalogue
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
How to use it
Take samuraigpt/muapi-chibi-collage-effect from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.