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Found 24 Skills
Detect and mask PII (names, emails, phones, SSN, addresses) in text and CSV files. Multiple masking strategies with reversible tokenization option.
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.
tokenization과 context window를 중심으로 긴 입력 처리 한계와 실무 대응 방법(분할, 요약, 우선순위화)을 학습시키는 모듈.
Apply when handling credit card data, implementing secureProxyUrl flows, or working with payment security and proxy code. Covers PCI DSS compliance, Secure Proxy card tokenization, sensitive data handling rules, X-PROVIDER-Forward-To header usage, and custom token creation. Use for any payment connector that processes credit, debit, or co-branded card payments to prevent data breaches and PCI violations.
Pendle Finance yield tokenization plugin. Buy or sell fixed-yield PT tokens, trade YT yield tokens, provide or remove AMM liquidity, and mint or redeem PT+YT pairs. Trigger phrases: buy PT, sell PT, buy YT, sell YT, Pendle fixed yield, Pendle liquidity, add liquidity Pendle, remove liquidity Pendle, mint PT YT, redeem PT YT, Pendle positions, Pendle markets, Pendle APY. Chinese: 购买PT, 出售PT, 购买YT, 出售YT, Pendle固定收益, Pendle流动性, Pendle持仓, Pendle市场
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.
Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.
Use when "tokenizers", "HuggingFace tokenizer", "BPE", "WordPiece", or asking about "train tokenizer", "custom vocabulary", "tokenization", "subword", "fast tokenizer", "encode text"
Pendle Finance yield tokenization plugin. Buy or sell fixed-yield PT tokens, trade YT yield tokens, provide or remove AMM liquidity, and mint or redeem PT+YT pairs. Trigger phrases: buy PT, sell PT, buy YT, sell YT, Pendle fixed yield, Pendle liquidity, add liquidity Pendle, remove liquidity Pendle, mint PT YT, redeem PT YT, Pendle positions, Pendle markets, Pendle APY. Chinese: 购买PT, 出售PT, 购买YT, 出售YT, Pendle固定收益, Pendle流动性, Pendle持仓, Pendle市场
Integrate Cardcom payment processing and Israeli invoice generation into applications, covering Low Profile payments, tokenization, recurring billing, and automatic tax invoice/receipt creation per Israeli law. Use when user asks to accept payments via Cardcom, generate Israeli invoices with payments, set up "slikat ashrai" with hashbonit, handle recurring billing (hora'ot keva), or mentions "Cardcom", "CardCom API", "Low Profile", Israeli payment with invoicing, or needs combined payment plus document generation. Targets the REST API V11. Do NOT use for Tranzila integration (use tranzila-payment-gateway), general accounting, or non-payment queries.
对产品标题进行分词分析，提取词频、场景词、人群词、材质词等属性维度。当用户想分析产品标题、提取标题高频词、进行标题分词、发现场景词或人群词、对比不同商品的标题关键词用法、基于词频优化Listing标题、识别一组ASIN中的常见属性规律、title tokenization, word frequency analysis, scene keyword extraction, audience keyword analysis, title optimization, attribute keyword extraction, keyword frequency时触发此技能。即使用户未明确说"标题分析"，只要其需求涉及将产品标题拆解为有意义的词组、统计关键词频率或按提取的属性对商品分组，也应触发此技能。
Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.