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Found 38 Skills
World-class alternative data and sentiment analysis for trading - social media, news, on-chain data, positioning. Extract alpha from information others miss. Use when "sentiment, alternative data, social media trading, news trading, twitter signals, on-chain, whale watching, fear greed, positioning, " mentioned.
Real-time sentiment analysis on Twitter/X using Grok. Use when analyzing social sentiment, tracking market mood, or measuring public opinion on topics.
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.
Generate research questions from economic phenomena
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.
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"
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 when "tokenizers", "HuggingFace tokenizer", "BPE", "WordPiece", or asking about "train tokenizer", "custom vocabulary", "tokenization", "subword", "fast tokenizer", "encode text"
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