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 JanYork · Claude Skill · ★ 54
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm-wiki-cli safe to install? View the security audit →
LWC — Proactive Memory for AI Agents Agent-driven · Persistent · Source-grounded English · 简体中文 · 日本語 · Español · Português (Brasil) · Français · Рус
| Stars | 54 |
| Forks | 6 |
| Language | Rust |
| Category | Claude Skill |
| License | Apache-2.0 |
| Quality Score | 65.965390309326/100 |
| Last Updated | 2026-09-13 |
| Created | 2026-07-29 |
| Platforms | claude-code, cli, codex, gemini, rust |
| Est. Tokens | ~28k |
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llm-wiki-cli is Agent-driven proactive memory CLI for AI agents — autonomously recall, maintain, and evolve persistent, source-grounded knowledge across sessions.. It is categorized as a Claude Skill with 54 GitHub stars.
llm-wiki-cli is primarily written in Rust. It covers topics such as agent-memory-system, agent-skills, ai-agents.
You can find installation instructions and usage details in the llm-wiki-cli GitHub repository at github.com/JanYork/llm-wiki-cli. The project has 54 stars and 6 forks, indicating an active community.
llm-wiki-cli is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to llm-wiki-cli on Agent Skills Hub include awesome-ai-agent-skills, agent-skills, kasetto. 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: