What is the difference between a focus search and a semantic query?

A focus search (--focus) follows the actual import tree of a specific file, while a semantic query (--query) finds code based on conceptual similarity, even if they aren't directly linked by imports.

How does Ospack improve AI coding tasks?

Ospack identifies and packages only the relevant parts of a codebase, including dependencies and semantically related logic, ensuring the AI has the exact context needed without hitting token limits.

Which output format is best for Claude Code?

The XML format is generally recommended for Claude as it provides a clear, structured hierarchy that is easy for language models to parse and reference.

Can I use Ospack for large codebases?

Yes, Ospack is designed to handle large projects by allowing you to set maximum file limits and focusing on specific entry points to maintain relevant context.

Ospack Semantic Context Packer

Name: Ospack Semantic Context Packer
Author: Tylerbryy

byTylerbryy

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Herramientas para Desarrolladores

Gathers comprehensive codebase context using intelligent import resolution and semantic search to streamline AI-assisted development.

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Ospack is a specialized context-gathering utility that helps developers and AI agents navigate complex codebases by packing relevant files and their dependencies into structured formats. By combining traditional import resolution with advanced semantic search, it allows users to locate specific logic or conceptual patterns—such as error handling or database connections—across an entire project. This skill is particularly effective for refactoring legacy code, debugging deep-seated issues, or quickly gaining an architectural understanding of unfamiliar repositories.

Características Principales

Support for XML, compact Markdown, and function-level chunk outputs
Conceptual semantic search to find code by meaning rather than keywords
Configurable exploration depth and file limits for large-scale projects
Focus-based packing to isolate context around specific entry points
1 GitHub stars
Intelligent import resolution for mapping full dependency chains

Casos de Uso

Exploring unfamiliar repositories to understand architectural dependencies
Locating specific logic patterns across a codebase using natural language queries
Building deep context for complex refactoring or feature implementation tasks

Acerca de

Características Principales

Support for XML, compact Markdown, and function-level chunk outputs
Conceptual semantic search to find code by meaning rather than keywords
Configurable exploration depth and file limits for large-scale projects
Focus-based packing to isolate context around specific entry points
1 GitHub stars
Intelligent import resolution for mapping full dependency chains

Casos de Uso

Exploring unfamiliar repositories to understand architectural dependencies
Locating specific logic patterns across a codebase using natural language queries
Building deep context for complex refactoring or feature implementation tasks