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Data Retention Mapper
by
vx：17605205782
· GitHub ↗
· v1.0.0
· MIT-0
239
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Install in OpenClaw
/install data-retention-mapper
Description
梳理数据保留周期、落盘位置、清理责任与过期处置建议。；use for data-retention, governance, privacy workflows；do not use for 替代正式合规意见, 隐瞒敏感存储位置风险.
README (SKILL.md)
数据留存地图师
你是什么
你是“数据留存地图师”这个独立 Skill，负责：梳理数据保留周期、落盘位置、清理责任与过期处置建议。
Routing
适合使用的情况
- 帮我画一张数据留存地图
- 整理保留周期和落盘位置
- 输入通常包含：数据类型、存储位置、保留要求
- 优先产出：数据资产、存储位置、改进建议
不适合使用的情况
- 不要替代正式合规意见
- 不要隐瞒敏感存储位置风险
- 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。
工作规则
- 先把用户提供的信息重组成任务书，再输出结构化结果。
- 缺信息时，优先显式列出“待确认项”，而不是直接编造。
- 默认先给“可审阅草案”，再给“可执行清单”。
- 遇到高风险、隐私、权限或合规问题，必须加上边界说明。
- 如运行环境允许 shell / exec，可使用：
python3 "{baseDir}/scripts/run.py" --input \x3C输入文件> --output \x3C输出文件>
- 如当前环境不能执行脚本，仍要基于
{baseDir}/resources/template.md与{baseDir}/resources/spec.json的结构直接产出文本。
标准输出结构
请尽量按以下结构组织结果：
- 数据资产
- 存储位置
- 保留周期
- 清理责任
- 风险点
- 改进建议
本地资源
- 规范文件：
{baseDir}/resources/spec.json - 输出模板：
{baseDir}/resources/template.md - 示例输入输出：
{baseDir}/examples/ - 冒烟测试：
{baseDir}/tests/smoke-test.md
安全边界
- 适合作为治理底稿。
- 默认只读、可审计、可回滚。
- 不执行高风险命令，不隐藏依赖，不伪造事实或结果。
Usage Guidance
This skill appears to do what it says: generate structured data‑retention reports from supplied inputs or local directories. Before running: (1) inspect scripts/run.py (it is bundled) if you want to confirm behavior (it only reads files and writes reports, and contains patterns to detect risky snippets), (2) avoid passing top-level or sensitive system paths as the --input to prevent accidental disclosure of unrelated files, (3) use --dry-run or run in an isolated workspace if you are testing, and (4) review any generated output before sharing externally because report contents may include snippets from scanned files. If you need to audit a live system, sanitize inputs or run the skill against a copy of the data.
Capability Analysis
Type: OpenClaw Skill
Name: data-retention-mapper
Version: 1.0.0
The data-retention-mapper skill bundle is a legitimate tool for data governance and privacy auditing. The core logic in scripts/run.py is limited to local file processing, CSV summarization, and scanning for security patterns (such as hardcoded secrets or dangerous shell commands) to generate structured Markdown reports. The SKILL.md instructions provide clear safety boundaries for the AI agent, explicitly prohibiting high-risk system changes or the concealment of risks, and the code relies solely on Python standard libraries without any network or persistence mechanisms.
Capability Assessment
Purpose & Capability
Name/description (data retention mapping) match the included resources: a template, spec, examples, and a Python script that generates structured reports or audits. The only required binary is python3, which is proportionate to the stated purpose.
Instruction Scope
SKILL.md limits the skill to read-only, reviewable outputs and instructs running the bundled script or using the templates if execution is unavailable. The included script can recursively scan directories and many file types (markdown, code, CSV, etc.) when given a directory as input — this is reasonable for audits, but it means the agent (or user) must avoid passing sensitive system paths (e.g., /, home directories with secrets) unless intended. The skill does not instruct reading unrelated environment variables or contacting external endpoints.
Install Mechanism
There is no install spec (instruction-only plus an included script). No external downloads, package installs, or third-party registries are invoked. This is low-risk from an install perspective.
Credentials
The skill requests no environment variables or credentials. The resources and script operate on local files provided as input; nothing requires access to cloud keys or unrelated secrets.
Persistence & Privilege
always is false and the skill does not request permanent platform privileges. The script can write an output file (normal for a report generator) but does not modify other skills or system-wide agent settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install data-retention-mapper - After installation, invoke the skill by name or use
/data-retention-mapper - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of data-retention-mapper.
- Provides structured mapping of data retention periods, storage locations, cleanup responsibilities, and expiry handling recommendations.
- Designed for use in data retention, governance, privacy, and audit workflows.
- Outputs include: data asset lists, storage locations, retention periods, risks, and actionable improvement suggestions.
- Does not replace formal compliance advice; highlights boundaries and risk when needed.
- Supports review drafts and executable checklists, with explicit handling of missing information and high-risk scenarios.
Metadata
Frequently Asked Questions
What is Data Retention Mapper?
梳理数据保留周期、落盘位置、清理责任与过期处置建议。；use for data-retention, governance, privacy workflows；do not use for 替代正式合规意见, 隐瞒敏感存储位置风险. It is an AI Agent Skill for Claude Code / OpenClaw, with 239 downloads so far.
How do I install Data Retention Mapper?
Run "/install data-retention-mapper" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Data Retention Mapper free?
Yes, Data Retention Mapper is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Data Retention Mapper support?
Data Retention Mapper is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux, win32).
Who created Data Retention Mapper?
It is built and maintained by vx：17605205782 (@52yuanchangxing); the current version is v1.0.0.
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