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osgrep

Transform natural-language queries into semantically relevant code results across repositories.

7|3|Updated Jan 15, 2026
Search Tags:#semantic-search#code-search#vector-search#rrf#colbert#hybrid-ranking
One-click install
npx skills add https://github.com/Zpankz/mcp-skillset --skill osgrep-zpankz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: osgrep
Source: https://github.com/Zpankz/mcp-skillset/tree/main/osgrep
Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill osgrep-zpankz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Code search often relies on exact token matches, missing conceptual similarities across languages and architectures in large codebases.

Core Features & Use Cases

  • Semantic, meaning-based search across codebases and languages
  • Hybrid ranking combining vector similarity with traditional BM25 for precise results
  • Claude Code workflow integration with structured outputs and scoring

Quick Start

Index your project and run a semantic query to explore meaning-based results.

Frequently Asked Questions about osgrep

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

FAQPage Schema
How does semantic code search work across multi-language repositories?▼

Semantic code search transforms natural-language queries into concept-level results by applying vector similarity and hybrid ranking. It surfaces meaning-based implementations across multi-language repositories instead of relying on exact token matches.

What's the best way to find cross-language code patterns using natural language?▼

Using semantic code search with hybrid ranking combines BM25 and vector similarity to find cross-language patterns. This approach surfaces concept-level implementations from natural-language queries, bypassing exact token matching limitations.

Can I integrate semantic code search with Claude Code workflows?▼

Yes, semantic code search supports Claude Code integration by providing structured JSON outputs and scoring filters. This allows you to pipeline search results directly into developer tools and automated workflows.

Do I need jq to run semantic code search pipelines?▼

Yes, jq is required as a dependency to parse and filter the JSON output generated by semantic code search. It enables you to build pipelines with other developer tools by processing structured search results.

Why does traditional code search miss conceptual similarities in large codebases?▼

Traditional code search relies on exact token matches, missing conceptual similarities across languages and architectures. Semantic code search solves this by using vector similarity to find meaning-based implementations across large codebases.

How do I filter semantic code search results by relevance scoring?▼

Semantic code search outputs structured JSON with relevance scores from its hybrid ranking system. You can apply scoring filters directly or use jq to parse the JSON output and threshold results for precise code retrieval.

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