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Generate, compose, and edit product images, posters, illustrations, and social graphics through the paid Beatra service. The workflow covers reviewing upload files and estimated costs before each paid call, then checking results and actual charges.
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Generate standalone images or supporting assets for presentations, websites, and videos through iPolloWork's image service. The skill follows requested visual details, selects a suitable authorized model, and saves the resulting image in the workspace.
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Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
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Install and use the official AI Image Generator package, pinned by digest, for paid hosted work on the Beatra service.
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Select, integrate, and operate multi-model image-generation gateways with model-specific schema discovery, version policy, asynchronous jobs, webhooks, spend approval, safe inputs and artifacts, data-governance review, and billing observability. Use for comparing or building against fal.ai, Replicate, or Together AI image APIs, including controlled failover; do not use for direct model-provider APIs, local inference, training, dedicated endpoint provisioning, video generation, or general image editing.
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Unified image-generation engine for the product-shots ecosystem. Dispatches to the right model family (OpenAI gpt-image-2 / Gemini gemini-3-pro-image-preview Nano Banana Pro) with one parameterised script for text-to-image and image-to-image. Primary backend: OmniMaaS / Cloubic gateway (https://api.omnimaas.com/v1); also supports any OpenAI-SDK-compatible gateway via fallback env vars. Auto-resizes oversized reference images to prevent edge-proxy timeouts. Use when the user says "generate an image", "make a picture", "create a visual", "render this", "edit this image", "做一张图", "生成图片", "改这张图" — or when invoked as the image-generation backend by another product-shots skill (main-image / detail-page / ad-creative / social-post / multi-angle). NOT a creative-direction skill: takes a prompt + optional reference images, returns a file path.
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Use when the user wants Agnes AI text, image, or video generation and should execute it through the agnes-ai-cli command line instead of hand-writing raw HTTP requests.
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Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
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Generate mobile app screen concepts and connected screen flows for iOS, Android, or cross-platform products. The skill emphasizes readable text, consistent colors and navigation, and clean phone mockups. It produces images rather than implementation code.
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Create character, scene, and product images from structured descriptions of style, composition, lighting, and color. Guide generation with reference images and aspect ratio settings, using a Python script connected to a supported image generation service.
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Create PowerPoint presentations by planning the content and visual style, generating each slide as an AI image, and assembling a PPTX file. Each new slide references the previous image to keep the presentation visually consistent.
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Create and edit images or generate short videos with xAI Grok. The skill explains how to write prompts, combine source images, assign reference images to video roles, and check generation progress before downloading results.
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Build prompts for FLUX image generation and editing, with guidance on model selection, lighting, composition, text, and precise colors. It also covers structured scene descriptions using JSON and editing with multiple reference images.
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Generate images from written instructions and edit saved or uploaded images using them as references. Continue refining the latest image in a conversation, adjusting details such as brightness, background, or colors while specifying what to preserve.
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Generate images and videos through Atlas Cloud by checking each model’s input requirements before submission. Guides the agent through sending a request, checking progress within a time limit, and downloading and validating the resulting media.
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Turn article ideas into hand-drawn, 16:9 illustrations featuring Grav, a floating character. Plan image placement and visual metaphors, then generate white-background sketches with sparse English labels and review their consistency.
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Generate realistic photo-style images with Google AI Studio for social posts and educational materials. Adjust aspect ratios, lighting, settings, and subtle imperfections to create a natural smartphone-photo look suited to each use.
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Create images from text descriptions, edit existing pictures, or combine multiple images. The script selects an image service from configured API keys and saves results in the workspace, with options for size, quality, and aspect ratio.
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Create or improve GPT-Image2 image prompts using a style library. Select suitable templates, then specify composition, materials, text, aspect ratio, and unwanted details in a prompt ready to copy.
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Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
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Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
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Create logos, illustrations, product images, and other visuals from text, or edit and combine reference images. The skill guides generation through Hive, waiting for results, and delivering saved images, with costs charged to Hive credits.
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Write and improve beauty and skincare posts for 小红书 using product details and target audiences. Create titles, body text, relevant tags, and image-generation prompts that match the product and the post's visual style.
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Turn image briefs into structured prompts for GPT-Image-2 using reusable JSON templates, organized fields for visual requirements. Specify composition, text, lighting, and constraints for product images, posters, infographics, and other design work.
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