Total 50,678 skills, AI & Machine Learning has 8495 skills
Showing 12 of 8495 skills
Use when designing custom voices with Alibaba Cloud Model Studio CosyVoice customization models, especially cosyvoice-v3.5-plus or cosyvoice-v3.5-flash, from a voice prompt plus preview text before using the returned voice_id in TTS.
Expert GPU optimization for modern consumer GPUs (8-24GB VRAM). Use this skill when you need to optimize GPU training, speed up CUDA code, reduce OOM errors, tune XGBoost for GPU, migrate NumPy to CuPy, make a model faster, manage GPU memory, optimize VRAM usage, or benchmark PyTorch. Covers mixed precision, gradient checkpointing, XGBoost GPU acceleration, CuPy/cuDF migration, vectorization, torch.compile, and diagnostics. NVIDIA GPUs only. PyTorch, XGBoost, and RAPIDS frameworks.
DEPRECATED: Use the model's native extended thinking instead. Structured, reflective problem-solving through sequential chain-of-thought reasoning that replaced the Sequential Thinking MCP server.
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.
Create new skills, modify and improve existing skills, and measure skill performance. Enhanced version with quick commands. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Triggers on phrases like "make a skill", "create a new skill", "build a skill for", "improve this skill", "optimize my skill", "test my skill", "turn this into a skill", "skill description optimization", or "help me create a skill".
Simulator mindset: Instead of asking "What do you think?", ask "Who knows this best in the world? What would they say?". Trigger words: super brain, top expert, world-class, best minds, who knows this best
Iteratively improve any output until measurable criteria are met. Use when the user wants to refine existing work against specific standards — whether it's code, prose, data, config, or any other artifact. Triggers on phrases like "improve this", "make it better", "iterate", "refine", "keep improving", "not good enough yet", "optimize this", "polish this", "tighten this up", or when the user provides criteria and wants repeated improvement until they're satisfied. Also use when the user gives feedback on output and expects you to keep refining, even if they don't say "improve" explicitly.
Generate audio visualization videos using each::sense AI. Create waveforms, spectrum analyzers, particle effects, 3D visualizations, and beat-synced animations from audio files.
(project - Skill) Generate AI videos using Volcengine Jimeng Video 3.0 Pro API. Use when users request video generation from text prompts or images, including text-to-video, image-to-video, or any AI-powered video creation. Triggers include "generate video", "create video", "AI video", "Jimeng video", "text to video", "image to video", or any request involving AI-powered video generation from descriptions.
Market overview. Use this skill whenever the user asks about overall market. Trigger phrases include: how is the market, market overview, what is happening in crypto. MCP tools: info_marketsnapshot_get_market_overview, info_coin_get_coin_rankings, info_platformmetrics_get_defi_overview, news_events_get_latest_events, info_macro_get_macro_summary.
Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
Implement approved OpenSpec proposal using DAG-scheduled multi-agent parallel execution