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 →
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
| Stars | 210 |
| Forks | 84 |
| Language | Rust |
| Category | Codex Skill |
| Quality Score | 67.462347764183/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-01 |
| Created | 2026-05-12 |
| Platforms | claude-code, cli, codex, rust |
| Est. Tokens | ~19k |
These tools work well together with semble_rs for enhanced workflows:
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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.
semble_rs is primarily written in Rust. It covers topics such as ai-agent, bm25, claude-code.
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.
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.
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: