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 iwe-org · MCP Server · ★ 1.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is iwe safe to install? View the security audit →
IWE - Memory system for you and your AI agents Turn your thinking into queryable context IWE turns a directory of markdown files into a knowledge graph — a connected structure you browse from your editor and your AI queries from the command line. Same files, same links, two interfaces. No cloud, no database, no lock-in. Version everything with git. IWE is for people who want database-style queries on their notes — "all drafts under this subtree", "every accepted decision in Q1" — without moving them into an actual database. Write in Markdown, structure with links, give AI agents the tools to navigate your knowledge. IWE it
| Stars | 1,654 |
| Forks | 79 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 67.9194794151526/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-19 |
| Created | 2024-09-20 |
| Platforms | cli, mcp, rust |
| Est. Tokens | ~25k |
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iwe is Markdown knowledge graph — LSP for your editor, CLI + MCP memory for your AI agents. It is categorized as a MCP Server with 1.7k GitHub stars.
iwe is primarily written in Rust. It covers topics such as ai-agents, ai-memory, cli.
You can find installation instructions and usage details in the iwe GitHub repository at github.com/iwe-org/iwe. The project has 1.7k stars and 79 forks, indicating an active community.
iwe is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to iwe on Agent Skills Hub include pi-llm-wiki, okf-skills, obsidian-second-brain. 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: