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Found 39 Skills
Real-time sentiment analysis on Twitter/X using Grok. Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics.
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.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
Extract structured advertising campaign parameters from natural language input provided by advertisers. This skill should be used when analyzing advertising requirements, campaign briefs, or ad requests that need to be converted into structured data. Supports both creating new campaigns and updating existing campaigns with additional information. Identifies missing information and provides helpful guidance for completing campaign requirements.
Generate research questions from economic phenomena
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Use to interpret qualitative feedback, trends, and risks across community channels.
Use when "HuggingFace Transformers", "pre-trained models", "pipeline API", or asking about "text generation", "text classification", "question answering", "NER", "fine-tuning transformers", "AutoModel", "Trainer API"
Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.
Coordinate smart-home actions across existing integrations with clear dry-run and safety confirmation.
AI text humanization: reduce AI-detection patterns, natural phrasing, tone adjustment
分析抖音视频评论情绪、情感和整体口碑。当用户想了解评论是正面的还是负面的、分析评论区整体舆情、评估视频是否受欢迎，或提取评论洞察时，使用此技能。