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Privacy Shield
by
xiaobu2020
· GitHub ↗
· v1.0.1
· MIT-0
177
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0
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Active Installs
2
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Install in OpenClaw
/install privacy-shield
Description
Manages sensitive data access by marking resources with privacy levels and enforcing share, export, and usage restrictions based on a centralized registry.
Usage Guidance
This skill appears to do what it says: a local privacy registry and CLI that checks and logs decisions. Before installing or invoking it, consider: (1) it will read/write data/privacy-registry.json and data/privacy-audit.jsonl in the agent workspace (or the directory implied by WORKSPACE) — review and set appropriate file permissions; (2) confirm you are comfortable allowing the agent to execute the included Python script (it runs locally and does not contact external endpoints); (3) review registry contents and audit logs for sensitive items before sharing them with other tools or people; (4) if you set WORKSPACE, ensure it points to the intended workspace so the registry/audit files land where you expect. No credentials or network calls were found in the code.
Capability Analysis
Type: OpenClaw Skill
Name: privacy-shield
Version: 1.0.1
The 'Privacy Shield' skill is a legitimate utility designed to help an AI agent manage sensitive data through a local registry and audit system. The Python script `scripts/registry.py` implements standard file-based CRUD operations for privacy rules and includes a permission-checking mechanism without any evidence of data exfiltration, remote execution, or malicious prompt injection.
Capability Assessment
Purpose & Capability
Name/description (privacy marking, enforcement, and audit) matches the included CLI script and SKILL.md instructions. No unrelated environment variables, binaries, or external services are required.
Instruction Scope
SKILL.md instructs agents to run the included script before any data-output operation; the script reads/writes local files (data/privacy-registry.json and data/privacy-audit.jsonl) under the WORKSPACE (or repo-root) — this is expected for the stated purpose but means the agent will access and modify workspace files and create audit logs.
Install Mechanism
No install spec or external downloads are present; the skill is instruction-only with a bundled Python script. No network installs or archive extraction are used.
Credentials
The skill does not require credentials or extra environment variables. It optionally respects WORKSPACE to locate registry/audit files, which is reasonable for a local file-based tool.
Persistence & Privilege
always is false and the skill does not request persistent platform privileges. It will read/write its own registry and audit files only; nothing in the code modifies other skills or system-wide settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install privacy-shield - After installation, invoke the skill by name or use
/privacy-shield - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
SKILL.md rewritten in English; added audit --deny-only, glob support, image-manager integration
v1.0.0
Initial release: privacy marking system with glob support, audit logging, and image-manager integration
Metadata
Frequently Asked Questions
What is Privacy Shield?
Manages sensitive data access by marking resources with privacy levels and enforcing share, export, and usage restrictions based on a centralized registry. It is an AI Agent Skill for Claude Code / OpenClaw, with 177 downloads so far.
How do I install Privacy Shield?
Run "/install privacy-shield" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Privacy Shield free?
Yes, Privacy Shield is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Privacy Shield support?
Privacy Shield is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Privacy Shield?
It is built and maintained by xiaobu2020 (@xiaobu2020); the current version is v1.0.1.
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