Core Context Compressor

Purpose

Compress long conversation histories into structured, token-efficient summaries for continuing work within context window limits. Extracts decisions, file changes, configuration values, and next steps into bullet-point format optimized for AI agent consumption.

LLMs have finite context windows (~32K-200K tokens). As conversations grow over dozens of exchanges, quality degrades because relevant information is buried. This skill produces a lossy-but-critical compression sacrificing implementation detail while preserving every decision, file change, configuration value, and unresolved question.

Agent Protocol

Trigger

"compress context", "context summary", "token save", "compression", "condense", "summarize conversation", "context budget", "reduce tokens", "context window"