Total 50,706 skills, AI & Machine Learning has 8496 skills
Showing 12 of 8496 skills
Transcribe audio files to text using local speech recognition. Triggers on: "转录", "transcribe", "语音转文字", "ASR", "识别音频", "把这段音频转成文字".
Generate AI videos using the Pollo AI API. Supports 13 leading models (Kling, Sora, Runway, Veo, Pixverse, Hailuo, Vidu, Luma, Pika, Wan, Seedance, Hunyuan, Pollo) with 50+ versions. It also supports task polling, credit cost estimation, and credit balance checks. Use this skill whenever the user wants to generate an AI video from text or image, use any AI video model, check Pollo credits, or mentions Pollo AI, pollo.ai, or any of the supported model names. Even if the user just says "generate a video" or "make me a short clip" without mentioning Pollo, this skill should be used.
Generate YouTube videos and Shorts using each::sense AI. Create faceless videos, explainers, tutorials, product reviews, compilations, and more optimized for YouTube's formats and best practices.
Transcribe audio to text using Sarvam AI's Saaras model. Handles speech recognition, transcription, and voice interfaces for 23 Indian languages. Supports 5 output modes, auto language detection, WebSocket streaming, and batch diarization. Use when converting speech to text or building voice-enabled apps.
Expert skill for OmniVoice, a massively multilingual zero-shot TTS model supporting 600+ languages with voice cloning and voice design capabilities.
Build and use free-code, the open-source fork of Claude Code CLI with telemetry removed, guardrails stripped, and all experimental features unlocked.
On-device, real-time multimodal AI voice and vision assistant powered by Gemma 4 E2B and Kokoro TTS, running entirely locally via FastAPI WebSocket server.
Autonomous LLM training optimization with GPU support. Runs 5-minute training experiments, measures val_bpb, keeps improvements or reverts — repeat forever. Use this skill when the user asks to "train a model autonomously", "optimize LLM training", "run ML experiments", "autoresearch with GPU", "optimize val_bpb", "autonomous ML training", "LLM pretraining loop", "setup ML autoresearch", "GPU training experiments", "pretrain from scratch", "speed up training", "lower my loss", "GPU optimization", "CUDA training", or mentions "train.py", "prepare.py", "bits per byte", "val_bpb", "NVIDIA GPU training", "RTX training", "H100 training", "autonomous model training", "consumer GPU training", "low VRAM training". Always use this skill when the user wants to autonomously optimize any ML training metric.
Use when generating videos with Model Studio DashScope SDK using Wan video generation models (wan2.6-t2v, wan2.6-i2v-flash, wan2.6-i2v and regional variants). Use when implementing or documenting video.generate requests/responses, mapping prompt/negative_prompt/duration/fps/size/seed/reference_image/motion_strength, or integrating video generation into the video-agent pipeline.
Triggered automatically at the start of each new top-level conversation to establish the general principle of "seeking truth from facts", and select downstream skills for subsequent tasks only when clearly applicable. Skip this skill if you are a delegated sub-agent performing a single specific task. English: Trigger at the start of each new top-level conversation to establish the core methodology and select downstream skills only when clearly useful. Skip this skill when you are a delegated sub-agent handling a narrow, concrete task.
Agent skill for agentic-payments - invoke with $agent-agentic-payments
Set up and maintain a persistent, LLM-managed knowledge base for a digital health project — turning clinical observations, papers, interviews, and planning docs into a compounding, interlinked wiki.