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 zircote · Claude Skill · ★ 55
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rlm-rs Recursive Language Model (RLM) CLI for Claude Code - handles long-context tasks via chunking and recursive sub-LLM calls. Based on the RLM pattern from arXiv:2512.24601, enabling analysis of documents up to 100x larger than typical context windows. Features Hybrid Semantic Search: Combined semantic + BM25 search with RRF fusion Auto-Embedding: Embeddings generated automatically during load (BGE-M3 model) Pass-by-Reference: Retrieve chunks by ID for efficient
| Stars | 55 |
| Forks | 6 |
| Language | Rust |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 72.9870470233701/100 |
| Open Issues | 13 |
| Last Updated | 2026-08-10 |
| Created | 2026-01-19 |
| Platforms | claude-code, cli, rust |
| Est. Tokens | ~24k |
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rlm-rs is Rust CLI implementing the Recursive Language Model (RLM) pattern for Claude Code. Process documents 100x larger than context windows through intelligent chunking, SQLite persistence, and recursive sub. It is categorized as a Claude Skill with 55 GitHub stars.
rlm-rs is primarily written in Rust. It covers topics such as ai-tools, chunking, claude.
You can find installation instructions and usage details in the rlm-rs GitHub repository at github.com/zircote/rlm-rs. The project has 55 stars and 6 forks, indicating an active community.
rlm-rs is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to rlm-rs on Agent Skills Hub include tokf, .claude, claudekit. 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.
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