Can I use claudemem to find unused code?

Yes, starting with version 0.4.0, the 'dead-code' command identifies symbols with no callers and low PageRank that are likely safe to remove.

How does claudemem differ from standard grep or search?

Unlike text-based search, claudemem uses Tree-sitter for AST parsing, allowing it to understand the actual structure of the code, such as identifying definitions versus calls.

What is the benefit of PageRank in code analysis?

PageRank identifies symbols that are most central to the codebase based on how many other parts of the code reference them, helping you focus on the most important logic first.

What languages does claudemem support?

It supports a wide variety of languages including TypeScript, Go, Python, and more, leveraging the universal Tree-sitter parsing engine.

Claudemem Semantic Code Search

Name: Claudemem Semantic Code Search
Author: tianzecn

bytianzecn

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開発者ツール

Analyzes and navigates complex codebases using AST-based structural mapping, PageRank importance scoring, and semantic search.

概要

Claudemem is a powerful code analysis tool designed to help developers and AI agents navigate large-scale repositories with surgical precision. By combining Tree-sitter AST parsing with vector-based semantic search, it provides a deep understanding of code structure that goes far beyond simple text matching. It features unique PageRank-driven importance scoring to highlight critical symbols, automated call-graph analysis for impact assessment, and specialized commands to detect dead code or testing gaps. This skill is essential for performing safe refactors, onboarding to unfamiliar codebases, and maintaining high code hygiene in complex software projects.

主な機能

Automated detection of dead code and high-importance test coverage gaps
Semantic vector search with BM25 ranking for intelligent code discovery
PageRank importance scoring to identify central architectural symbols
Call graph analysis to trace callers and callees for impact assessment
1 GitHub stars
AST-based structural mapping for high-level repository overviews

ユースケース

Performing impact analysis before refactoring to identify all affected call sites
Onboarding to a large codebase by mapping structural entry points and dependencies
Improving code quality by identifying unused symbols and untested critical logic

概要

主な機能

Automated detection of dead code and high-importance test coverage gaps
Semantic vector search with BM25 ranking for intelligent code discovery
PageRank importance scoring to identify central architectural symbols
Call graph analysis to trace callers and callees for impact assessment
1 GitHub stars
AST-based structural mapping for high-level repository overviews

ユースケース

Performing impact analysis before refactoring to identify all affected call sites
Onboarding to a large codebase by mapping structural entry points and dependencies
Improving code quality by identifying unused symbols and untested critical logic