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Found 16 Skills
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.
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.
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.
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.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
End-to-end Stellar development playbook. Covers Soroban smart contracts (Rust SDK), Stellar CLI, JavaScript/Python/Go SDKs for client apps, Stellar RPC (preferred) and Horizon API (legacy), Stellar Assets vs Soroban tokens (SAC bridge), wallet integration (Freighter, Stellar Wallets Kit), smart accounts with passkeys, status-sensitive zero-knowledge proof patterns, testing strategies, security patterns, and common pitfalls. Optimized for payments, asset tokenization, DeFi, privacy-aware applications, and financial applications. Use when building on Stellar, Soroban, or working with XLM, Stellar Assets, trustlines, anchors, SEPs, ZK proofs, or the Stellar RPC/Horizon APIs.
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.
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
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
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.
Detect and mask PII (names, emails, phones, SSN, addresses) in text and CSV files. Multiple masking strategies with reversible tokenization option.
Train your own GPT-2 level LLM for under $100 using nanochat, Karpathy's minimal hackable harness covering tokenization, pretraining, finetuning, evaluation, inference, and chat UI.