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 psinetron · Codex Skill · ★ 143
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is echoes-vault-codex safe to install? View the security audit →
EchoesVault Agent-neutral, repository-local Markdown memory bootstrapped from Codex. <img src="https://img.shields.io/badge/Buy%20Me%20a%20Coffee-Support-FFDD00?logo=buymeacoffee&logoColor=000000" alt="Support EchoesVault on Buy Me
| Stars | 143 |
| Forks | 1 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.5827798373848/100 |
| Last Updated | 2026-09-04 |
| Created | 2026-08-15 |
| Platforms | codex, python |
| Est. Tokens | ~16k |
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echoes-vault-codex is Persistent memory plugin for Codex. Obsidian-style knowledge base that survives across sessions. It is categorized as a Codex Skill with 143 GitHub stars.
echoes-vault-codex is primarily written in Python. It covers topics such as agentic-memory, agentic-memory-architecture, ai-agent.
You can find installation instructions and usage details in the echoes-vault-codex GitHub repository at github.com/psinetron/echoes-vault-codex. The project has 143 stars and 1 forks, indicating an active community.
echoes-vault-codex is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to echoes-vault-codex on Agent Skills Hub include pi-llm-wiki, remnic, Overture. 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: