How is the ROI for optimizations calculated?

ROI is calculated by dividing the total projected token savings by the estimated number of days required to implement the architectural change.

What is the primary purpose of the Context Optimizer skill?

The Context Optimizer aggregates findings from other detection tools to provide a roadmap for reducing token usage and maximizing the efficiency of the AI's context window.

Does this skill perform its own code analysis?

No, it is an aggregator skill that uses results from other plugins like agent-chain-analyzer and script-extractor to synthesize a high-level optimization strategy.

What kind of architectural patterns does it help improve?

It focuses on reducing bloat in agent chains, removing excessive inline logic, and optimizing hybrid agent structures to ensure only necessary data is sent to the model.

Context Optimizer

Name: Context Optimizer
Author: fractary

byfractary

•

Productivity & Workflow

Aggregates context optimization opportunities and calculates total potential token savings from architectural improvements.

About

The Context Optimizer skill is a high-level diagnostic tool designed to maximize the efficiency of Claude's context window. By synthesizing data from multiple specialized detection skills—including chain analyzers, script extractors, and hybrid agent detectors—it provides a comprehensive overview of how architectural refactoring can reduce token consumption. It calculates a specific Return on Investment (ROI) for each optimization opportunity, allowing developers to prioritize high-impact architectural changes that significantly lower operational costs and improve model responsiveness.

Key Features

Provides structured JSON output for automated optimization workflows
2 GitHub stars
Calculates precise token savings and context reduction percentages
Analyzes agent chains, inline logic, and hybrid agent architectures for bloat
Prioritizes refactoring tasks based on an ROI-driven effort-vs-savings metric
Aggregates data from multiple specialized diagnostic skills into a unified report

Use Cases

Refactoring complex monolithic prompts into efficient, modular multi-agent systems
Reducing operational costs and latency in high-volume AI agent deployments
Identifying 'low-hanging fruit' for context window optimization in legacy codebases

About

Key Features

Provides structured JSON output for automated optimization workflows
2 GitHub stars
Calculates precise token savings and context reduction percentages
Analyzes agent chains, inline logic, and hybrid agent architectures for bloat
Prioritizes refactoring tasks based on an ROI-driven effort-vs-savings metric
Aggregates data from multiple specialized diagnostic skills into a unified report

Use Cases

Refactoring complex monolithic prompts into efficient, modular multi-agent systems
Reducing operational costs and latency in high-volume AI agent deployments
Identifying 'low-hanging fruit' for context window optimization in legacy codebases