context-engineering-framework

Token optimization involves measuring current context consumption, applying compression techniques to reduce token count, and allocating budget across workflow stages. This Skill provides token counting, lossless and lossy compression, and dynamic budget allocation to keep multi-agent systems within constraints while preserving critical information.

Lossless compression preserves all semantic content while reducing tokens through summarization and structural reorganization. Lossy compression removes lower-priority details to achieve greater token savings. Choose lossless when fidelity is critical; use lossy when token budget is tight and some information loss is acceptable.

Generate a structured handoff document that summarizes compressed context, remaining token budget, and task state for downstream agents. This preserves essential information across agent transitions without reprocessing the full context, enabling efficient long-running workflows.

Yes. Context compression optimizes RAG performance by managing token budgets during document retrieval and summarization. Compress retrieved passages and apply budget allocation to balance search result quantity against token limits, improving both cost and response quality.

Token budget alerts notify you when context approaches limits. Apply compression passes and dynamic budget reallocation to stay within constraints. If still over budget, use lossy compression or prioritize highest-value content; handoff documents ensure critical state survives the reduction.

No. Token budgeting is a practical workflow discipline—measure usage, apply compression, allocate budget—without requiring deep model internals knowledge. The Skill handles the technical counting and compression; you manage budget allocation based on task priorities.