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RedactKit - AI Privacy Scrubber

该 Skill 通过 Python 库与命令行工具在本地扫描文本,识别邮箱、电话、API 密钥、信用卡号、SSN、IP 及自定义正则模式,按敏感级别替换为占位符并保存映射;可在 AI 处理后依据映射还原原始值,支持批量目录处理与报告输出,全程无网络调用。

theshadowrose
theshadowrose
热度 54.06
最近更新 2026-05-11 17:30:48
分类:安全依赖:代码执行 / 读写文件应用场景:数据处理 / 自动化脚本 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add redact-kit
Metadata
name
RedactKit - AI Privacy Scrubber
description
Scan your data before sending it to AI. Detect and redact PII, secrets, and sensitive info. Reversible, local, zero network calls.
author
@TheShadowRose
version
1.0.1
tags
["privacy","redaction","pii","security","data-protection","scrubber"]
license
MIT

RedactKit - AI Privacy Scrubber

Use this skill to scan text for sensitive data before sending it to any AI API. Detects and redacts PII, API keys, passwords, phone numbers, emails, and custom patterns. Fully reversible — original values can be restored after the AI responds.

When to Use This Skill

Invoke this skill when:

  • A user wants to send documents to an AI without exposing personal data
  • You need to redact API keys, tokens, or credentials from text before logging or sharing
  • Preprocessing text for AI analysis that must remain GDPR/HIPAA compliant
  • You want to restore redacted values after receiving an AI response

Quick Start

from redact_kit import RedactionEngine
from redact_restore import RestorationEngine

engine = RedactionEngine(sensitivity_level='medium')

text = "Email john@example.com or call 555-123-4567. API key: sk-abc123xyz"
result = engine.redact(text)

print(result.redacted_text)
# "Email [EMAIL-1] or call [PHONE-1]. API key: [API-KEY-1]"

# Save mapping so you can restore later
engine.save_mapping(result.mapping_id, 'mapping.json')

# Send result.redacted_text to AI safely
# Then restore originals from the AI response
restore_engine = RestorationEngine()
restore_engine.load_mapping('mapping.json')
restored = restore_engine.restore(ai_response, result.mapping_id)

What RedactKit Detects

Category Examples
Email addresses user@domain.com
Phone numbers 555-123-4567, +1 (555) 123-4567
API keys & tokens sk-..., Bearer ..., ghp_...
Credit card numbers 4111 1111 1111 1111
Social Security Numbers 123-45-6789
IP addresses 192.168.1.1
Passwords in text password: mysecret
Custom patterns Define your own regex patterns

Custom Patterns

from redact_patterns import CustomPatternManager

manager = CustomPatternManager()
manager.add_pattern(
    name="employee_id",
    pattern=r"EMP-\d{6}",
    placeholder="[EMPLOYEE_ID]",
    category="internal",
    sensitivity="high"
)

Restoration

from redact_restore import RestorationEngine

restorer = RestorationEngine()
restorer.load_mapping('mapping.json')  # must load before restoring

# After AI processes the redacted text, restore originals
original_response = restorer.restore(
    redacted_text="Contact [EMAIL-1] for details",
    mapping_id=result.mapping_id
)
# "Contact john@example.com for details"

Sensitivity Levels

Patterns are tagged by sensitivity:

  • critical — API keys, passwords, SSNs → always redact
  • high — emails, phone numbers → redact by default
  • medium — IP addresses, internal IDs → configurable
  • low — general identifiers → opt-in

Control which levels to redact at engine construction:

# Redact only critical + high patterns (skip medium/low)
engine = RedactionEngine(sensitivity_level='high')
result = engine.redact(text)

Zero Network Calls

All redaction and restoration happens locally. No data leaves your machine. No external dependencies beyond Python standard library.

See README.md for full pattern reference and batch processing documentation.