← 返回 Skills 市场
Sandwrap
作者
Ruben Quispe
· GitHub ↗
· v1.0.0
1663
总下载
3
收藏
4
当前安装
1
版本数
在 OpenClaw 中安装
/install sandwrap
功能描述
Run untrusted skills safely with soft-sandbox protection. Wraps skills in multi-layer prompt-based defense (~85% attack prevention). Use when: (1) Running third-party skills from unknown sources, (2) Processing untrusted content that might contain prompt injection, (3) Analyzing suspicious files or URLs safely, (4) Testing new skills before trusting them. Supports manual mode ('run X in sandwrap') and auto-wrap for risky skills.
安全使用建议
This skill is an instruction-only 'soft' sandbox: it provides detailed policies and code examples but ships no code to actually enforce them. That means the protection it offers depends entirely on the agent/platform following its prompts and on any platform-level interception you may already have. Before using it on sensitive data: (1) confirm your platform can intercept and enforce tool calls and path restrictions (the skill assumes this capability); (2) do not rely on Sandwrap for high-value secrets — use a VM/container or a vetted isolation mechanism instead; (3) examine where sandbox-config.json and sandwrap-output/ would live and who can read/write them; (4) test the skill with benign but adversarial-looking inputs to validate that the platform enforces the rules the skill describes; and (5) if you need stronger guarantees, request an implementation (code that runs on the platform and performs tool interception) or prefer a real OS-level sandbox.
功能分析
Type: OpenClaw Skill
Name: sandwrap
Version: 1.0.0
The OpenClaw skill 'sandwrap' is designed as a security defense mechanism to protect against prompt injection and malicious skill execution. Its primary purpose, as detailed across SKILL.md, CLAWHUB-README.md, and references/architecture.md, is to wrap untrusted skills in a multi-layered, prompt-based 'soft sandbox'. The architecture document explicitly outlines defensive measures such as dynamic delimiters, instruction hierarchy, tool allowlists, human-in-the-loop approvals, and output verification. Crucially, it includes pseudo-code and rules to prevent data exfiltration (e.g., detecting secrets, large base64 blobs, blocking internal/private IPs), block malicious execution (e.g., path traversal, executable file writes), and counter various prompt injection techniques (e.g., meta-instructions, roleplay, encoded payloads). All content consistently describes a security-focused tool with no evidence of malicious intent or risky capabilities beyond its stated defensive purpose.
能力评估
Purpose & Capability
Name/description align with a prompt-based 'soft sandbox'. However, the SKILL.md and architecture docs claim code-level enforcement (tool interception before execution, path checks, rate limiting) and provide implementation snippets, but there is no install spec or code in the package to implement those enforcement points. That mismatch means the skill can only rely on the agent following its prompts rather than actually enforcing restrictions at a system or platform level.
Instruction Scope
The runtime instructions direct the agent to sanitize inputs, intercept and block tool calls, consult and modify sandbox-config.json, and write to sandwrap-output/. Those actions reference filesystem config and state that are not declared in the registry metadata. Because this is an instruction-only skill, the agent's adherence depends entirely on the platform honouring the rules; the skill itself doesn't provide an enforcement mechanism or independent checks.
Install Mechanism
No install spec and no code files are included. That minimizes the risk of arbitrary code being dropped/executed, but it also means the documented protections are only policy-level instructions rather than implemented controls.
Credentials
The skill requests no environment variables or credentials (good). However, it references config files (sandbox-config.json) and output paths (sandwrap-output/) without declaring required config paths or explaining access patterns. This is not a secret-exfiltration flag, but it is a mismatch between claimed behavior and declared requirements.
Persistence & Privilege
always is false and the skill is user-invocable (normal). The skill describes auto-wrap behavior and reading/writing a sandbox-config.json, which implies persistent configuration if the platform implements it — but the skill does not itself create or store persistent artifacts. If the platform implements persistent auto-wrap, consider the implications; the skill alone does not request elevated privileges.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install sandwrap - 安装完成后,直接呼叫该 Skill 的名称或使用
/sandwrap触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: Soft-sandbox protection for running untrusted skills. 5 defense layers, 4 presets, ~85% attack prevention.
元数据
常见问题
Sandwrap 是什么?
Run untrusted skills safely with soft-sandbox protection. Wraps skills in multi-layer prompt-based defense (~85% attack prevention). Use when: (1) Running third-party skills from unknown sources, (2) Processing untrusted content that might contain prompt injection, (3) Analyzing suspicious files or URLs safely, (4) Testing new skills before trusting them. Supports manual mode ('run X in sandwrap') and auto-wrap for risky skills. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1663 次。
如何安装 Sandwrap?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install sandwrap」即可一键安装,无需额外配置。
Sandwrap 是免费的吗?
是的,Sandwrap 完全免费(开源免费),可自由下载、安装和使用。
Sandwrap 支持哪些平台?
Sandwrap 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Sandwrap?
由 Ruben Quispe(@rubenaquispe)开发并维护,当前版本 v1.0.0。
推荐 Skills