Skill UI

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

Designs and implements production-grade RAG systems with document chunking, embeddings, vector stores, hybrid search, reranking, and evaluation, ideal for knowledge-grounded AI apps that need semantic retrieval, similarity search, and context augmentation.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

This comprehensive checklist guides developers through the necessary steps to deploy a robust and reliable Grammarly API integration in a production environment. It covers critical aspects such as secure credential storage, rate limit handling, implementing text chunking, designing error resilience patterns (circuit breakers, retries), and establishing comprehensive monitoring and alerting systems to ensure high content quality and system uptime.

This guide provides production-ready patterns for integrating with the Grammarly API using both TypeScript and Python. It covers essential architectural aspects, including typed client implementation, robust token management (OAuth flow), and efficient text chunking for processing large documents. Use this when building scalable, reliable Grammarly-powered features into your application.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

This skill covers the architecture of intelligent agent memory, including short-term context windows and long-term vector stores. It explores cognitive frameworks like CoALA to organize semantic, episodic, and procedural memory. Focuses on retrieval strategies, chunking, and embeddings to ensure agents remember and recall information effectively across sessions.

This skill covers splitting extracted text into chunks suitable for LLM context windows or RAG ingestion. It includes configuring chunk size, overlap, and selecting chunker types like markdown, yaml, or semantic. Users can use the standalone chunk command or enable inline chunking during extraction. It supports token-based sizing for specific models and provides configuration options via CLI, config files, or Python API.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.

Vexor CLI enables semantic file discovery within large repositories. Instead of manual browsing or exact text matching, it locates files based on intent, implementation, or definition. It supports various search modes like code-aware chunking and Markdown outlines, allowing developers to quickly find relevant code or documentation. Use it for locating entrypoints, config logic, or specific features efficiently.

Xberg is a document intelligence library supporting 107 formats including PDF, Office, and images. It extracts text, tables, metadata, and images using OCR. Available for Python, Node.js, Rust, and CLI. Supports batch processing, configuration options like chunking and OCR, and custom plugins. Ideal for developers building document processing applications.