context-optimization

Name: context-optimization
Availability: InStock
Author: ken-cavanagh-glean

Compress, mask, cache, and partition context windows with Python implementations.

4|Updated Dec 23, 2025

Search Tags:#memory-management #KV-cache #token-management #sub-agent-partitioning #agent-performance #context-optimization

Ready to run

npx skills add https://github.com/ken-cavanagh-glean/fieldkit --skill context-optimization-ken-cavanagh-glean

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-optimization
Source: https://github.com/ken-cavanagh-glean/fieldkit/tree/main/plugins/context-engineering/skills/context-engineering/context-optimization
Command: npx skills add https://github.com/ken-cavanagh-glean/fieldkit --skill context-optimization-ken-cavanagh-glean

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Context windows limit what an AI agent can consider at once, causing degraded performance and higher costs on long-running tasks. This Skill provides techniques to maximize usable context without increasing model burden.

Core Features & Use Cases

Compaction: summarize near-limit content to preserve decisions and state.
Observation Masking: replace verbose tool outputs with concise references while keeping retrievability.
KV-Cache Optimization: reuse stable context blocks to reduce recomputation across requests.
Context Partitioning: split work across sub-agents to maintain small, efficient contexts in each thread.

Quick Start

Review the Context Optimization Reference in references/optimization_techniques.md.
Run the included Python script to experiment: python3 scripts/compaction.py with a sample payload or integrate into your agent loop.
Monitor token usage and latency; adjust thresholds to balance quality and cost.

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 usage when agent conversations grow too long?▼

Context optimization compresses and partitions long conversations to extend effective context window. Techniques include summarizing near-limit content, masking verbose tool outputs with concise references, reusing stable context blocks via KV-cache, and splitting work across sub-agents to maintain small, efficient contexts in each thread.

What's the best way to manage KV-cache for repeated agent requests?▼

KV-cache optimization reuses stable context blocks across requests to reduce recomputation and latency. The included Python scripts demonstrate caching patterns that preserve critical signals while cutting redundant processing overhead in long-running agent workflows.

Can I compress agent context without losing critical information?▼

Yes. Compaction summarizes near-limit content to preserve decisions and state, while observation masking replaces verbose tool outputs with concise retrievable references. This approach maintains signal fidelity while freeing token capacity for new reasoning.

How do I split large agent tasks across multiple sub-agents?▼

Context partitioning divides work across sub-agents to keep each thread's context small and efficient. This reduces per-request token load and latency while enabling complex workflows that would exceed a single agent's context window.

When should I use context optimization instead of a larger model?▼

Optimization is preferred for production workflows where cost and latency matter. It works within existing model limits to maximize usable context, reduce recomputation, and maintain performance without upgrading model capacity or incurring higher inference costs.