Data Security Compliance And Regulated Data

Community

vaquarkhan

data-security-compliance-and-regulated-data

Guides agents through regulated-data security and compliance workflows for PII, PCI, HIPAA, PHI, and similar obligations. Use when data products handle sensitive fields, regulated records, control evidence, or audit-bound publish paths.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`data-security-compliance-and-regulated-data`
Stars	60
Forks	8
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by vaquarkhan on GitHub. Read the source before you install it.

Installation

Install the Data Security Compliance And Regulated Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/data-security-compliance-and-regulated-data
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/data-security-compliance-and-regulated-data

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skills.git /tmp/data-engineering-agent-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-agent-skills/skills/data-security-compliance-and-regulated-data .claude/skills/data-security-compliance-and-regulated-data

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Security Compliance And Regulated Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Security Compliance And Regulated Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Security Compliance And Regulated Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Security, Compliance, And Regulated Data

Overview

Use this skill when the data platform handles regulated or highly sensitive data and the change must satisfy engineering, governance, and audit expectations together. It helps agents treat controls, lineage, access, retention, deletion, evidence, and publish safety as one delivery surface.

When to Use

handling PII, PCI, HIPAA, PHI, or other regulated data classes
moving sensitive data between raw, curated, serving, and partner-facing layers
designing masking, tokenization, encryption, or access controls
proving that published data meets audit or policy requirements
changing lineage, retention, deletion, or evidence paths for regulated assets
preparing a system for compliance review, internal audit, or control sign-off

Do not treat regulated-data handling as a documentation-only exercise.

Workflow

Classify the data and obligations. Clarify:
- what fields are sensitive
- whether the scope includes PII, PCI, HIPAA, PHI, or contractual controls
- what the allowed usage, retention, and deletion rules are
- which teams own the control and audit evidence
Map the data flow end to end. Include:
- ingestion and landing zones
- transformation and quality layers
- serving, BI, feature, reverse-ETL, and extract paths
- replication, caching, backup, and replay surfaces
- lineage and consumer touchpoints
Define the required controls explicitly. Controls may include:
- encryption at rest and in transit
- tokenization or masking
- row-level and column-level access
- environment separation
- secrets and key management
- restricted publish paths
- retention and deletion enforcement
- audit logging and evidence capture
Align implementation with policy and platform behavior. Verify that code, SQL, orchestration, and platform configuration all enforce the same control intent.
Define compliance evidence and release gates. Require:
- lineage updates
- ownership and escalation path
- test or validation evidence for masking, access, and deletion behavior
- publish approval criteria when regulated data leaves the producing boundary
Plan for incidents and replay. Regulated data failures need:
- containment path
- audit trail preservation
- replay or backfill safety
- communication path for impacted consumers or control owners

Common Rationalizations

Rationalization	Reality
"The warehouse already encrypts everything."	Encryption alone does not satisfy access, minimization, retention, lineage, or audit evidence obligations.
"This is only internal analytics data."	Internal use does not remove obligations for `PII`, `PCI`, `HIPAA`, or contractual data-handling controls.
"The source system already handles compliance."	Downstream copies, extracts, feature stores, and dashboards create new control surfaces.
"We can document the control evidence later."	Missing evidence often turns routine changes into release blockers or audit findings.

Red Flags

regulated fields are copied without a control matrix
lineage is incomplete for a sensitive publish path
retention or deletion rules stop at one storage layer
replay or backfill behavior for sensitive data is undefined
access controls are described in docs but not enforced in code or platform config
compliance approval depends on tribal knowledge instead of explicit evidence

Verification

Sensitive fields and regulatory scope are classified explicitly
End-to-end lineage and storage locations are mapped
Access, masking, encryption, retention, and deletion controls are implemented or clearly planned
Audit evidence and release gates are defined for the regulated-data path
Incident, replay, and rollback paths preserve both correctness and compliance obligations

Frequently asked questions

What does the Data Security Compliance And Regulated Data AI skill do?

Why use Data Security Compliance And Regulated Data on TypingMind?

Because you install it once and use it with any model. Data Security Compliance And Regulated Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Security Compliance And Regulated Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/data-security-compliance-and-regulated-data. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Security Compliance And Regulated Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Security Compliance And Regulated Data?

As many as you like. As long as a model supports skills, you can use Data Security Compliance And Regulated Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Security Compliance And Regulated Data AI skill free?

Yes. It is published on GitHub by vaquarkhan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Weights And Biases

NousResearch

OrganizationPopular

W&B: log ML experiments, sweeps, model registry, dashboards.

Faiss

Fast vector similarity search at billion scale.

Serving Llms Vllm

vLLM: high-throughput LLM serving, OpenAI API, quantization.

Huggingface Hub

HuggingFace hf CLI: search/download/upload models, datasets.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Security, Compliance, And Regulated Data

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

Related AI Skills

Set up your own AI workspace now