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Found 71 Skills
Build text-to-speech applications using Qwen3-TTS, a powerful speech generation system supporting voice clone, voice design, and custom voice synthesis. Use when creating TTS applications, generating speech from text, cloning voices from audio samples, designing new voices via natural language descriptions, or fine-tuning TTS models. Supports 10 languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian).
Receive and verify OpenAI webhooks. Use when setting up OpenAI webhook handlers for fine-tuning jobs, batch completions, or async events like fine_tuning.job.completed, batch.completed, or realtime.call.incoming.
Collaboration Process for UI Style Modifications. Used when users request page style changes, layout adjustments, or UI detail tweaks. The structured process of "Screenshot Localization → Current Status Description → Option Selection → Code Modification → Fine-tuning" reduces communication deviations and avoids token waste.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Use this skill when building production LLM applications, implementing guardrails, evaluating model outputs, or deciding between prompting and fine-tuning. Triggers on LLM app architecture, AI guardrails, output evaluation, model selection, embedding pipelines, vector databases, fine-tuning, function calling, tool use, and any task requiring production AI application design.
Build Next.js web applications with Google Gemini Nano Banana image generation APIs (gemini-2.5-flash-image, gemini-3-pro-image-preview). Use when creating image generators, editors, galleries, or any app integrating conversational image generation with server actions, API routes, and storage. Use for "image generation app", "nano banana", "text to image", "AI image generator", or "gemini image". Do NOT use for non-Gemini models, Python/Go backends, model fine-tuning, or image classification/input tasks.
Implement comprehensive image editing capabilities in Blazor applications using the Syncfusion Image Editor component. Use this skill when implementing image editing, annotations, transformations, cropping, filtering, zooming, and panning features. Supports annotations (text, shapes, freehand), transformations (crop, rotate, flip, resize), effects (filters, fine-tuning), toolbar customization, and keyboard shortcuts.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Forge a complete lobster soul solution for OpenClaw AI Agent. Based on user preferences or random gacha, output identity positioning, soul description (SOUL.md), role-based bottom-line rules, name, and avatar generation prompts. If the current environment provides an audited image generation skill, it can automatically generate avatar images with unified style. Use this when users need to create, design or customize OpenClaw lobster souls. Not applicable for: fine-tuning existing SOUL.md, character design for non-OpenClaw platforms, pure tool-type Agent without personality. Trigger words: 龙虾灵魂, 虾魂, OpenClaw 灵魂, 养虾灵魂, 龙虾角色, 龙虾定位, 龙虾剧本杀角色, 龙虾游戏角色, 龙虾 NPC, 龙虾性格, 龙虾背景故事, lobster soul, lobster character, 抽卡, 随机龙虾, 龙虾 SOUL, gacha.
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
Use this skill when crafting, iterating, or optimizing prompts for LLMs including zero-shot, few-shot, chain-of-thought, role prompting, structured output, and prompt chaining. Not for fine-tuning or training models. Not for evaluating model quality across benchmarks.
Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.