Does this skill help with high API costs?

Yes. By reducing the number of tokens sent in each request through optimization and caching strategies, it significantly lowers the cost of running long-running agentic systems.

How does observation masking save tokens?

It replaces verbose tool outputs that have already been processed with compact references or brief summaries, preventing irrelevant data from occupying valuable context space.

When should I use context partitioning?

Partitioning is best for complex tasks that can be broken down into sub-tasks. It allows sub-agents to operate in clean, isolated contexts without carrying the weight of the entire project history.

What is context compaction?

Context compaction is a technique where the agent summarizes previous conversation history and tool outputs when approaching token limits, reinitializing the window with a high-fidelity summary to preserve memory.

Context Optimization for AI Agents

Name: Context Optimization for AI Agents
Author: shipshitdev

byshipshitdev

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データサイエンスとML

Optimizes AI agent context windows using advanced compaction, masking, and partitioning techniques to improve performance and reduce token costs.

This skill provides a robust framework for managing the context constraints of LLM-based agents, enabling them to handle longer conversations and more complex tasks without hitting token limits. By implementing strategies like high-fidelity summarization, eliding verbose tool outputs, and delegating tasks to isolated sub-agents, it effectively triples context capacity. Ideal for production-scale systems where latency, cost, and long-term memory are critical, this skill ensures agents remain focused on relevant signals while systematically discarding noise and redundant information.

主な機能

01Strategic context compaction and high-fidelity summarization

02Trigger-based optimization logic for production scaling

03Sub-agent partitioning for isolated context execution

04KV-cache optimization for reduced latency and stable prefixing

0510 GitHub stars

06Observation masking to elide verbose tool outputs

ユースケース

01Scaling long-running autonomous agent sessions without performance decay

02Reducing operational API costs by minimizing unnecessary token usage

03Improving response speed in complex, multi-turn technical workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add shipshitdev/library context-optimization

For use in Claude.ai and ChatGPT

Download Skill