semble_rs — security grade SAFE, quality 67/100

Security audit verdict: SAFE · quality 67/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by johunsang · Codex Skill · ★ 210

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is semble_rs safe to install? View the security audit →

About semble_rs

semblers Fast and Accurate Code Search for Agents — in Rust Replaces grep / cat / read / ls and compresses build & CI output. Up to -99% tokens. Quickstart • Search • Tree • Digest • Deps / Impact • How it works • Benchmarks is a Rust port and superset

ai-agentbm25claude-codeclicode-searchcodexcoding-assistantcursordependency-graphdeveloper-tools

Quick Facts

Stars210
Forks84
LanguageRust
CategoryCodex Skill
Quality Score67.462347764183/100
Open Issues1
Last Updated2026-06-01
Created2026-05-12
Platformsclaude-code, cli, codex, rust
Est. Tokens~19k

Compatible Skills

These tools work well together with semble_rs for enhanced workflows:

  • codesearch — semantic(0.52)+complementary+rare_topics+same_lang+similar_pop+shared_platform (77%)
  • Vera — semantic(0.44)+complementary+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • octocode — semantic(0.38)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)

semble_rs alternative? Top 6 similar tools

Looking for a semble_rs alternative? If you're comparing semble_rs with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • octocode by Muvon · ⭐ 475

    Structural code intelligence for AI agents — semantic search, knowledge graphs, and a built-in MCP server in o

  • Vera by lemon07r · ⭐ 102

    Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tr

  • ops-codegraph-tool by optave · ⭐ 95

    Code intelligence CLI — function-level dependency graph across 34 languages, 34-tool MCP server for AI agents,

  • codegraph-rust by Jakedismo · ⭐ 885

    100% Rust implementation of code graphRAG with blazing fast AST+FastML parsing, surrealDB backend and advanced

  • rust-docs-mcp-server by Govcraft · ⭐ 291

    🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, use

  • stacklit by glincker · ⭐ 101

    One command gives AI agents instant codebase context. ~250 tokens replaces 50,000+ tokens of exploration. Auto

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Frequently Asked Questions

What is semble_rs?

semble_rs is Fast, AI-agent-native code search in Rust — hybrid BM25 + semantic, Tree-sitter AST chunking, dependency & impact analysis. Drop-in replacement for grep/cat/read/ls in Claude Code, Codex, Cursor, Aide. It is categorized as a Codex Skill with 210 GitHub stars.

What programming language is semble_rs written in?

semble_rs is primarily written in Rust. It covers topics such as ai-agent, bm25, claude-code.

How do I install or use semble_rs?

You can find installation instructions and usage details in the semble_rs GitHub repository at github.com/johunsang/semble_rs. The project has 210 stars and 84 forks, indicating an active community.

What are the best alternatives to semble_rs?

The top alternatives to semble_rs on Agent Skills Hub include octocode, Vera, ops-codegraph-tool. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Codex Skill tools