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Context Optimizer

Analyze token usage and compress prompts in long-running agent sessions.

6|5|Updated Mar 9, 2026
Search Tags:#memory management#token efficiency#context optimization#agent performance#prompt compression
One-click install
npx skills add https://github.com/OpenAnalystInc/Vibe-Marketer --skill context-optimizer-openanalystinc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Context Optimizer
Source: https://github.com/OpenAnalystInc/Vibe-Marketer/tree/main/.agents/skills/context-optimizer
Command: npx skills add https://github.com/OpenAnalystInc/Vibe-Marketer --skill context-optimizer-openanalystinc

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of optimizing token and context efficiency in long sessions, improving agent performance and reducing waste.

Core Features & Use Cases

  • Token Usage Analysis: Analyzes token counts and identifies bloated sections.
  • Prompt Compression: Reduces agent prompt length while preserving instruction fidelity.
  • Context Injection Optimization: Reviews injected context blocks for relevance and freshness.
  • Memory Management: Optimizes knowledge base entries for retrieval efficiency.
  • Use Case: Ideal for optimizing prompts and context in long-running sessions where efficiency is crucial.

Quick Start

Run the Context Optimizer with the task '🧠 Context Optimizer — Optimize session context for agent "SessionBot" in output/reports/optimization/'.

Frequently Asked Questions about Context Optimizer

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

FAQPage Schema
How do I reduce token waste in long-running agent sessions?▼

Token waste in long-running agent sessions is reduced by analyzing bloated prompt sections and compressing instructions while preserving fidelity. Optimizing memory management and context injection improves overall agent throughput.

What is context injection optimization and how does it work?▼

Context injection optimization reviews injected context blocks for relevance and freshness to improve agent performance. It ensures long-running sessions maintain efficient token usage by removing stale or bloated knowledge base entries.

Does prompt compression affect instruction fidelity for agents?▼

Prompt compression reduces agent prompt length while preserving instruction fidelity. It targets token efficiency by analyzing and trimming bloated sections, ensuring agents still follow instructions accurately in long sessions.

How do I optimize memory management for agent knowledge base entries?▼

Memory management for knowledge base entries is optimized by analyzing retrieval efficiency and freshness. Reviewing injected context blocks ensures long-running sessions maintain high throughput without wasting tokens on stale data.

What are the limitations of context optimization for long-running sessions?▼

Context optimization for long-running sessions requires knowledge of token efficiency rules and memory management techniques. It is limited by the need to preserve instruction fidelity during prompt compression and context block analysis.

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