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Found 66 Skills
Configure code chunking in GrepAI. Use this skill to optimize how code is split for embedding.
Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.
Document chunking implementations and benchmarking tools for RAG pipelines including fixed-size, semantic, recursive, and sentence-based strategies. Use when implementing document processing, optimizing chunk sizes, comparing chunking approaches, benchmarking retrieval performance, or when user mentions chunking, text splitting, document segmentation, RAG optimization, or chunk evaluation.
Process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performance
Expert guidance on document chunking strategies for RAG systems. Use this skill when designing how to split documents for vector embeddings. Activate when: chunking, chunk size, text splitting, document segmentation, overlap, semantic chunking, recursive splitting.
Use this skill whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation menus with many items. Trigger when designing OTP / verification UIs, formatting numeric strings, breaking long forms into sections, grouping nav items, or reviewing surfaces that "have too many things at once." Chunking is one of the foundational principles in 'Universal Principles of Design' (Lidwell, Holden, Butler 2003), grounded in Miller's classic working-memory research.
Use this skill when designing inputs or displays for numeric strings — phone numbers, OTP / verification codes, credit card numbers, account IDs, license keys, currency, dates, social security numbers. Trigger when picking a format for displaying numbers, designing OTP entry UIs, or formatting identifiers in tables and receipts. Sub-aspect of `chunking`; read that first.
Process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performance
Use when crawling web pages, extracting markdown content, or scraping website data with intelligent chunking and skeleton planning. Use when the user provides a URL or link to fetch or crawl.
Docling document parser for PDF, DOCX, PPTX, HTML, images, and 15+ formats. Use when parsing documents, extracting text, converting to Markdown/HTML/JSON, chunking for RAG pipelines, or batch processing files. Triggers on DocumentConverter, convert, convert_all, export_to_markdown, HierarchicalChunker, HybridChunker, ConversionResult.
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
UX patterns for complex forms including multi-step wizards, cognitive chunking (5-7 fields max), progressive disclosure, and conditional fields. Use when building checkout flows, onboarding wizards, or forms with many fields.