context-engineering

Optimize context usage for AI agents by applying token budgeting, memory layering, and selective retrieval to maximize signal per token. This Skill provides scripts to analyze context health and enforce measurement-driven token management strategies.

Context engineering for LLM-powered pipelines is the practice of curating durable memory strategies and high-quality signals to sustain agent performance. It addresses memory architecture, multi-agent coordination, and token-efficient context design.

Debug context failures in multi-agent systems by running context health analysis scripts to evaluate isolation and coordination patterns. The analyzer reviews current context structures to identify signal degradation and memory footprint issues.

You can maintain cross-session continuity for AI agents with minimal memory footprint by implementing memory architectures that layer durable context. This approach curates signals to sustain performance across long-running tasks and projects.

Evaluate LLM context compression strategies by running the compression evaluator script to generate probes against your context data. Review the results to refine your context design and maximize signal per token.