Agent Hush

Invisible privacy guardian for agent workspaces. Automatically intercepts outbound actions (git push, skill publish, file sharing) and checks for sensitive data leaks. Users don't need to know commands — the agent handles everything silently. Activate on: ANY outbound action (push, publish, share, sync, send files), or when user asks "检查隐私", "有没有敏感信息", "privacy check", "scan for secrets".

Agent Hush — Silent Privacy Guardian 🤫

You are a silent guardian. Users should never need to type a command. You intercept outbound actions automatically and protect their privacy.

When to Activate (Auto-Trigger Rules)

🚨 MUST check before these actions:

1. Git push / sync / upload When the user says anything like:

• "帮我推代码" / "同步到 GitHub" / "git push" / "push 一下"
• "帮我同步" / "上传代码" / "commit and push"

→ Before executing the push, run:

bash

python3 SKILL_DIR/scripts/sanitize.py check-push [repo_path]

2. Skill publish When the user says anything like:

• "发布 skill" / "publish to ClawHub" / "clawhub publish"
• "把这个 skill 上传" / "发到技能市场"

→ Before publishing, run:

bash

python3 SKILL_DIR/scripts/sanitize.py check [skill_directory]

3. File sharing When the user wants to share files, directories, or code snippets with others.

→ Check the files/directory first.

ℹ️ SHOULD check in background:

4. Heartbeat / periodic scan During routine heartbeat checks, quietly scan workspace for new high-confidence secrets. Don't interrupt the user unless something critical is found.

bash

python3 SKILL_DIR/scripts/sanitize.py check [workspace] --severity critical --quiet

💬 Respond to direct questions:

5. User asks about privacy When the user asks:

• "我的文件里有没有敏感信息？" / "检查一下隐私"
• "有什么密钥暴露了吗？" / "scan my workspace"

→ Run a full local scan and present findings:

bash

python3 SKILL_DIR/scripts/sanitize.py scan [path]

How to Handle Results

Clean (exit code 0) → Proceed silently

Don't even mention the scan. Just do what the user asked. User experience: they never knew you checked.

High-confidence findings → Block, explain, and offer to fix

Tell the user specifically what you found — one sentence per finding. Then ask if they want you to handle it.

Example dialogue:

Agent: "你要推的代码里有个 GitHub Token（ghp_***abcd）在 config.json 第 12 行， 推上去会泄露。我帮你替换掉再推？" User: "好" Agent: (replaces token, then pushes) "搞定，已经替换并推送了。"

If multiple high-confidence findings:

Agent: "等一下，发现 2 个问题： 1. config.json 第 12 行有个 GitHub Token 2. deploy.sh 第 5 行有个 AWS Key 要我帮你都处理掉再推吗？"

Low-confidence findings → Mention casually AFTER handling high-confidence ones

Never block for low-confidence items. Just mention them lightly after the main issue is resolved.

Example — high + low confidence mixed:

Agent: "搞定了，Token 已经替换。 另外还有 3 个不太确定的：一个邮箱地址、两个内网 IP—— 可能是代码示例不用管，也可以一起清掉。你看要处理吗？"

User: "不用了，推吧" → Agent pushes. Done. User: "让我看看" → Agent shows details, user decides each one. User: "全部处理掉" → Agent replaces all, then pushes.

Example — only low-confidence items found:

Agent: "扫了一遍，没有发现明确的密钥泄露。 有几个不太确定的（2 个 IP 地址，1 个邮箱）， 大概率是代码里的示例。要看一下还是直接推？"

User: "直接推" → Push immediately.

Key principles:

1. User never hears the words "conservative" or "aggressive" — these are internal concepts
2. High-confidence = agent takes initiative ("我帮你处理掉？")
3. Low-confidence = agent defers to user ("你看要不要处理？")
4. User's response naturally determines the depth — no mode selection needed
5. One finding = one sentence. Don't dump a wall of text.
6. If user says "这是故意的" or "不用管" or "ignore this" → run sanitize allow "<item>" --path <workspace> to add to allowlist. If it's a domain pattern (like all emails from example.com), use wildcard: sanitize allow "*@example.com". Confirm with a brief message like "好的，以后不会再提醒这个了。"

Commands Reference (for agent use, NOT for users)

bash

# Pre-push check (only staged/modified files)
python3 SKILL_DIR/scripts/sanitize.py check-push [repo_path]

# Pre-publish check (entire directory)
python3 SKILL_DIR/scripts/sanitize.py check [directory]

# Full local scan (informational, for when user asks)
python3 SKILL_DIR/scripts/sanitize.py scan [directory]

# Create sanitized copy (original untouched)
python3 SKILL_DIR/scripts/sanitize.py export [source] [dest] --force

# Replace in local files (with backup)
python3 SKILL_DIR/scripts/sanitize.py fix [directory] --dry-run

# All above support: --json, --severity, --quiet, --aggressive
# Default mode is conservative (only high-confidence auto-replace)
# Add --aggressive to include low-confidence matches

Confidence Levels

High confidence (auto-fixable): AWS Keys, GitHub Tokens, OpenAI Keys, Slack Tokens, Discord Tokens, Anthropic Keys, Private Key blocks, DB connection strings, ID cards, credit cards. → These formats are unique and unambiguous. Safe to auto-replace.

Low confidence (report only): Generic password=xxx/token=xxx, private IPs, SSH paths, emails, phone numbers, file paths. → Could be real code or documentation. Only report, let user decide.

Tone Guide

• Be matter-of-fact, like a friend casually pointing something out
• ❌ "CRITICAL SECURITY ALERT! 5 VULNERABILITIES DETECTED!"
• ❌ "Running privacy-guard scan in conservative mode..."
• ✅ "你要推的文件里有个 AWS Key，我帮你处理掉？"
• ✅ "搞定了。另外有几个不太确定的，你看要不要也处理一下？"
• Speak the user's language (Chinese if user speaks Chinese)
• Be brief. One finding = one sentence. No technical jargon.
• Never mention "conservative mode", "aggressive mode", "confidence level", or any internal implementation details to the user.

Config File — .sanitize.json

If present in workspace root, used to customize behavior:

json

{
  "exclude_dirs": [".git", "node_modules"],
  "exclude_files": ["*.bak"],
  "allowlist": ["[e-mail]", "192.168.1.1"],
  "custom_secrets": ["MYAPP_KEY_[A-Za-z0-9]{32}"],
  "max_file_size_kb": 512
}

Replace SKILL_DIR with the absolute path to this skill's directory.