name: skill sanitizer description: "First open source AI sanitizer with local semantic detection. 7 layers + code block awareness + LLM intent analysis. Catches prompt injection, reverse shells, memory tampering, encoding evasion, trust abuse. 85% fewer false positives in v2.1. Zero cloud — your prompts stay on your machine." user invocable: true metadata: openclaw: emoji: "🧤" homepage: "https://github.com/cyberxuan XBX/skill sanitizer"
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。
SkillHub CLI
skillhub install cyberxuan-xbx/skill-sanitizer打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。
The first open-source AI sanitizer with local semantic detection.
Commercial AI security tools exist — they all require sending your prompts to their cloud. Your antivirus shouldn't need antivirus.
This sanitizer scans any SKILL.md content before it reaches your LLM. 7 detection layers + optional LLM semantic judgment. Zero dependencies. Zero cloud calls. Your data never leaves your machine.
ignore previous instructions in "helpful" skillssafe-defender can contain eval(user_input)
| Layer | What It Catches | Severity |
|---|---|---|
| 1. Kill-String | Known platform-level credential patterns (API keys, tokens) | CRITICAL |
| 2. Prompt Injection | ignore previous instructions, role hijacking, system prompt override |
HIGH-CRITICAL |
| 3. Suspicious Bash | rm -rf /, reverse shells, pipe-to-shell, cron modification |
MEDIUM-CRITICAL |
| 4. Memory Tampering | Attempts to write to MEMORY.md, SOUL.md, CLAUDE.md, .env files | CRITICAL |
| 5. Context Pollution | Attack patterns disguised as "examples" or "test cases" | MEDIUM-HIGH |
| 6. Trust Abuse | Skill named safe-* or secure-* but contains eval(), rm -rf, chmod 777 |
HIGH |
| 7. Encoding Evasion | Unicode homoglyphs, base64-encoded payloads, synonym-based i... |
这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。
name: skill-sanitizer
description: "First open-source AI sanitizer with local semantic detection. 7 layers + code block awareness + LLM intent analysis. Catches prompt injection, reverse shells, memory tampering, encoding evasion, trust abuse. 85% fewer false positives in v2.1. Zero cloud — your prompts stay on your machine."
user-invocable: true
metadata:
openclaw:
emoji: "🧤"
homepage: "https://github.com/cyberxuan-XBX/skill-sanitizer"
---
# Skill Sanitizer
**The first open-source AI sanitizer with local semantic detection.**
Commercial AI security tools exist — they all require sending your prompts to their cloud. Your antivirus shouldn't need antivirus.
This sanitizer scans any SKILL.md content **before** it reaches your LLM. 7 detection layers + optional LLM semantic judgment. Zero dependencies. Zero cloud calls. Your data never leaves your machine.
## Why You Need This
- SKILL.md files are **prompts written for AI to execute**
- Attackers hide `ignore previous instructions` in "helpful" skills
- Base64-encoded reverse shells look like normal text
- Names like `safe-defender` can contain `eval(user_input)`
- Your agent doesn't know it's being attacked — it just obeys
## The 7 Layers
| Layer | What It Catches | Severity |
|-------|----------------|----------|
| 1. Kill-String | Known platform-level credential patterns (API keys, tokens) | CRITICAL |
| 2. Prompt Injection | `ignore previous instructions`, role hijacking, system prompt override | HIGH-CRITICAL |
| 3. Suspicious Bash | `rm -rf /`, reverse shells, pipe-to-shell, cron modification | MEDIUM-CRITICAL |
| 4. Memory Tampering | Attempts to write to MEMORY.md, SOUL.md, CLAUDE.md, .env files | CRITICAL |
| 5. Context Pollution | Attack patterns disguised as "examples" or "test cases" | MEDIUM-HIGH |
| 6. Trust Abuse | Skill named `safe-*` or `secure-*` but contains `eval()`, `rm -rf`, `chmod 777` | HIGH |
| 7. Encoding Evasion | Unicode homoglyphs, base64-encoded payloads, synonym-based i...Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
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