Index codebases with tree-sitter parsing and embeddings to surface duplicate code.
npx skills add https://github.com/tulgardinc/vibe-check --skill vibecheck
Please help me install this Agent Skill. Skill: vibecheck Source: https://github.com/tulgardinc/vibe-check/tree/main Command: npx skills add https://github.com/tulgardinc/vibe-check --skill vibecheck
Vibe Check automates the detection and surfacing of duplicated or near-duplicate code across a codebase using tree-sitter parsing and embeddings, helping teams reduce tech debt and improve maintainability.
Run vibec index to build the semantic index, then use vibec query or vibec scan to surface and act on duplicates in your project.
High-intent search queries and answers about installing and using this skill.
Code duplication detection works by parsing functions with tree-sitter and generating embeddings to measure similarity, surfacing near-matches via Jaccard-based token analysis. This local pipeline identifies semantic and structural overlaps to help you refactor and extract shared logic.
To scan a codebase for similar functions, run the index command to build a local semantic embedding cache, then execute a scan or query command to surface duplicated code. The CLI workflow applies exclusion rules and model checks for deterministic duplicate detection.
Yes, duplicate detection operates via an MCP server and CLI workflow using SQLite to store embedding caches locally. This ensures secure, deterministic operation without sending codebase data to external services.
Yes, tree-sitter parsing and embeddings are used together to detect near-duplicate code across a codebase. The similarity detection combines semantic embeddings with Jaccard-based token analysis to accurately surface candidates for refactoring or extraction.
Limitations include dependency on model and version checks for deterministic results, and the need to configure exclusion rules to avoid scanning irrelevant files. The embedding cache requires initial indexing time, and accuracy depends on the quality of the tree-sitter parsing and embedding models used.
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