ai-context-engineer

What problem does it solve?

Guides context engineering for LLM systems—assembling prompts, budgeting tokens, prioritizing sources, compressing history, caching, structured context blocks, and debugging context-related failures (lost instructions, overflow, distraction).

Core Features & Use Cases

• Context budgeting: allocate tokens across system, tools, retrieved context, and history to minimize waste.
• History compression: roll up long conversations while preserving goals and decisions.
• Context pipelines: build prompts and retrieval paths to feed agents with relevant context.
• Caching and prefetch: reuse stable prefixes and preload retrieved docs to reduce latency.
• Debugging context: instrument requests and reproduce context assembly for support.

Quick Start

Configure a base context policy with a system block, tool definitions, and a retrieval+history strategy, then verify token budgets and latency in a test session.