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mrsladoje mrsladoje

Sweet Search

AI community
SOURCE https://github.com/mrsladoje/sweet-search

Description

Local code search for AI agents: six fast, purpose-built tools that return ranked answers, not raw grep. Because maybe grep isn't all you need... 🍬

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

sweet-search — local code search for AI coding agents

**Local code search for AI coding agents.** Six fast, purpose-built tools that hand *Claude Code*, *Codex* & friends ranked answers, not raw grep. Zero API keys, 100% on-device.

*Maybe grep isn't all you need…* 🍬
Every coding agent today reaches for grep + Read by reflex. *sweet-search* challenges the narrative. 😎

[![npm](https://img.shields.io/npm/v/sweet-search?color=cb3837&label=npm)](https://www.npmjs.com/package/sweet-search) [![GitHub stars](https://img.shields.io/github/stars/mrsladoje/sweet-search?style=social)](https://github.com/mrsladoje/sweet-search/stargazers) [![license](https://img.shields.io/badge/license-Apache--2.0-blue)](LICENSE) [![node](https://img.shields.io/badge/node-%E2%89%A518-brightgreen)](package.json) [![platforms](https://img.shields.io/badge/platform-macOS%20%7C%20Linux-lightgrey)](#platform-support) [![inference](https://img.shields.io/badge/inference-100%25%20local-success)](#-gpu-accelerated-indexing-fully-local)

✨ Highlights

  • Hybrid retrieval — one of the six tools uses BM25F lexical + dense semantic + structural graph signals, fused per query and reranked by late-interaction
  • Agent-native by design — token-budgeted output tiers, an optional MCP server (and default zero-overhead CLI), and a GEPA-evolved system prompt — one init installs it into Claude Code by default (Codex, Gemini CLI, and Cursor via flags)
  • Indexed grep, ~10× faster than ripgrep — a sparse n-gram prefilter skips the files that provably can't match
  • ColBERT-style reranking, locally — per-token MaxSim late interaction on hand-written SIMD kernels
  • GPU-accelerated indexing — Apple Metal, CUDA, CoreML Neural Engine, or plain CPU via ORT; same engine, auto-selected
  • Never stale — incremental indexing keeps the index aligned with your working tree, uncom

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