context-optimization

Compress conversational history and mask tool outputs to reduce token usage.

1|Updated Feb 4, 2026

Search Tags:#prompt engineering #token efficiency #AI performance #cost reduction #context optimization #KV cache

Ready to run

npx skills add https://github.com/bthillerup/bens-garage-session-2 --skill context-optimization-bthillerup

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-optimization
Source: https://github.com/bthillerup/bens-garage-session-2/tree/main/.github/skills/context-optimization
Command: npx skills add https://github.com/bthillerup/bens-garage-session-2 --skill context-optimization-bthillerup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the challenge of limited context windows in AI models, which can lead to increased costs, latency, and reduced performance when handling large amounts of information.

Core Features & Use Cases

Context Compaction: Summarizes and reduces the size of conversational history and tool outputs.
Observation Masking: Strategically hides less critical tool outputs to save tokens.
KV-Cache Optimization: Improves inference efficiency by organizing cached data.
Context Partitioning: Divides work across sub-agents for isolated, focused contexts.
Use Case: When dealing with long-running agent systems or production environments where token costs and latency are critical, this skill helps maintain performance and reduce expenses.

Quick Start

Optimize the current context by applying compaction and masking strategies.

Frequently Asked Questions about context-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce token costs when my AI agent hits context window limits?▼

Reduce token costs at context window limits by applying context compaction, observation masking, and context partitioning to summarize history and isolate focused sub-agent contexts.

What is context compaction and how does it optimize AI performance?▼

Context compaction optimizes AI performance by summarizing and shrinking conversational history and tool outputs, directly reducing the token count processed during inference to lower latency.

How does KV-cache optimization improve inference efficiency for long-running agents?▼

KV-cache optimization improves inference efficiency for long-running agents by strategically organizing cached data, which minimizes redundant processing and accelerates response times in production systems.

Can I use context partitioning to manage large tool outputs in production environments?▼

You can use context partitioning in production environments to manage large tool outputs by dividing work across sub-agents, ensuring isolated and focused contexts that maintain performance.

What is the best way to mask less critical tool outputs for prompt engineering?▼

The best way to mask less critical tool outputs for prompt engineering is through observation masking, which strategically hides redundant data to save tokens without losing essential context.

When should I not use context optimization strategies for my AI system?▼

Avoid context optimization strategies when your AI system processes minimal conversational history or operates well below token limits, as the overhead of compaction and partitioning may outweigh latency benefits.