context-compression

To compress text within token limits, the Skill utilizes extractive summarization, abstractive summarization, key-point extraction, and selective pruning. It analyzes information density and validates retention to ensure critical context is preserved for AI tasks.

The best way to reduce chat history context is by applying selective pruning and key-point extraction. The Skill scores information density across text chunks, dynamically allocates token budgets, and prunes low-relevance sections to preserve essential conversational context.

Yes, context compression supports abstractive summarization for lengthy legal documents. It shrinks textual context to fit specified token budgets while validating information retention, ensuring crucial legal details are preserved for AI analysis.

You manage token budgets by dynamically allocating tokens across multiple context sources. The Skill analyzes information density, scores text chunks for relevance, and applies compression strategies to prioritize the most critical data for LLM retrieval.

Use extractive summarization to pull direct key sentences from text, whereas selective pruning removes low-density chunks entirely. The Skill selects between these compression strategies based on information density scoring and the required token budget constraints.