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tygwan
tygwanCOMMUNITY
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context-optimizer

Analyze loaded files, score relevance, and apply targeted loading strategies.

1|Updated Jan 9, 2026
Search Tags:#context#codebase#optimization#AI efficiency#token#loading#summaries
One-click install
npx skills add https://github.com/tygwan/AgenticREVIT --skill context-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-optimizer
Source: https://github.com/tygwan/AgenticREVIT/tree/main/.claude/skills/context-optimizer
Command: npx skills add https://github.com/tygwan/AgenticREVIT --skill context-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill reduces AI context loading size by pruning non-essential files, references, and summaries to minimize token usage and speed up development sessions.

Core Features & Use Cases

  • Analyze Context: Identify loaded files, docs, and assets that are essential for the current task.
  • Context Scoring: Rank items by relevance (1-10), dependencies, recency, and frequency.
  • Strategic Loading: Apply essential-only, focused, or summarized loading strategies to fit token budgets.
  • Use Case: When working with a large codebase and limited tokens, load only critical modules and relevant documentation to maintain context focus.

Quick Start

Run the Context Optimizer on a large repository to trim the current context to only essential files and summaries.

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 usage when loading a large codebase into AI context?▼

To reduce AI context loading size, score loaded files by relevance, dependencies, recency, and frequency on a 1-10 scale. This ranking identifies essential files to retain while pruning non-essential references from your multi-file project workspace.

What's the best way to load only essential modules for a multi-file project with limited tokens?▼

The best way to load essential modules with limited tokens is applying strategic loading strategies like essential-only, focused, or summarized loading. This fits configurable token budgets by retaining only critical modules and relevant documentation.

Can I optimize AI context loading for development sessions where token budgets are tight?▼

Yes, you can optimize AI context loading for tight token budgets by analyzing loaded files and applying targeted loading strategies. This trims the current context to only essential files, minimizing token consumption and accelerating responses.

Why does loading my full repository slow down AI responses and consume excessive tokens?▼

Loading your full repository slows AI responses because non-essential files and summaries inflate the active context size. Reducing these non-essential items minimizes token usage and directly speeds up AI processing during large codebase development.

Are there limitations to using context optimization strategies for large codebases?▼

A limitation of context optimization for large codebases is the risk of pruning files later deemed essential. Scoring relevance and applying targeted loading strategies require careful configuration of token budgets to avoid losing critical dependencies.

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