smart-context

Extract relevant context from codebase graphs using level-based queries.

Updated Aug 27, 2026
Ready to run
npx skills add https://github.com/monicajeon28/GMcruise --skill smart-context
๐Ÿ’ฌ Prompt for ChatGPT / Claude Chatโ–ผ
Please help me install this Agent Skill.
Skill: smart-context
Source: https://github.com/monicajeon28/GMcruise/tree/main/.claude/skills/smart-context
Command: npx skills add https://github.com/monicajeon28/GMcruise --skill smart-context

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

์ฝ”๋“œ๋ฒ ์ด์Šค ์ „์ฒด๋ฅผ ์ฝ์–ด ํ•„์š”ํ•œ ๋งฅ๋ฝ์„ ๋น ๋ฅด๊ฒŒ ์–ป๊ธฐ ์–ด๋ ต๊ณ , ๋ถˆํ•„์š”ํ•œ ์ •๋ณด๋กœ ํ† ํฐ์ด ๋‚ญ๋น„๋˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•ฉ๋‹ˆ๋‹ค. codebase-graph ๊ธฐ๋ฐ˜์œผ๋กœ ํ•„์š”ํ•œ ์ปจํ…์ŠคํŠธ๋งŒ ์„ ํƒ์ ์œผ๋กœ ์ถ”์ถœํ•ฉ๋‹ˆ๋‹ค.

Core Features & Use Cases

  • ์ปจํ…์ŠคํŠธ ๋ ˆ๋ฒจ ๊ธฐ๋ฐ˜ ์ถ”์ถœ: Level_0 ~ Level_4๊นŒ์ง€ ํ•„์š”ํ•œ ์ •๋ณด๋งŒ ์„ ํƒ์ ์œผ๋กœ ๋กœ๋“œ
  • ์ฝ”๋“œ๋ฒ ์ด์Šค ๊ทธ๋ž˜ํ”„์—์„œ ์‹œ๊ทธ๋‹ˆ์ฒ˜, ํ˜ธ์ถœ ๊ด€๊ณ„, ์˜์กด์„ฑ ๋“ฑ์„ ์ •ํ™•ํžˆ ์‹๋ณ„
  • ํ† ํฐ ์ ˆ๊ฐ๊ณผ ์ •ํ™•๋„ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋ถ€๋ถ„ ๋กœ๋”ฉ ์ „๋žต

Quick Start

target ํ•จ์ˆ˜/ํด๋ž˜์Šค๊ฐ€ ์–ด๋””์—์„œ ์‚ฌ์šฉ๋˜๋Š”์ง€ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด codebase-graph๋ฅผ ์‚ฌ์šฉํ•ด ๊ด€๋ จ ๋…ธ๋“œ๋“ค๋งŒ ๋กœ๋“œํ•ฉ๋‹ˆ๋‹ค.

Frequently Asked Questions about smart-context

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

FAQPage Schema
How do I reduce token usage when analyzing code dependencies and references?โ–ผ

Extract only relevant context from your codebase graph instead of loading entire files. Smart Context applies graph-based filtering on signatures, references, and dependencies to achieve ~72% token savings while maintaining ~92% accuracy, using configurable depth levels (0-5) and relation scopes.

What is context-level extraction and how does it work with codebase graphs?โ–ผ

Context-level extraction selectively loads information at different depthsโ€”Level 0 through Level 5โ€”from a codebase graph. This strategy identifies only signatures, call relationships, and dependencies relevant to your target function or class, minimizing unnecessary token consumption.

Can I use partial loading to understand where a function is called across my codebase?โ–ผ

Yes. Query the codebase graph to load only related nodes showing where your target function or class is referenced. This partial loading strategy reveals call chains and dependencies without loading entire files, saving tokens and focus.

What token savings and accuracy can I expect from selective context extraction?โ–ผ

Selective extraction from codebase graphs delivers approximately 72% token reduction with around 92% accuracy. Results depend on configuring appropriate depth levels and relation scopes to match your analysis scope.

Do I need to set up a codebase graph before using context extraction?โ–ผ

Yes. Smart Context requires a codebase-graph as inputโ€”a prerequisite that maps signatures, references, and dependencies across your project. This graph structure enables the selective, token-efficient extraction of relevant context.

What's the difference between extracting Level 0 versus Level 5 context?โ–ผ

Level 0 captures minimal direct information; higher levels (up to Level 5) progressively include related signatures, transitive dependencies, and call chains. Adjust depth to balance token budget against comprehensiveness for your analysis task.