Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vaquarkhan/data-engineering-agent-skills --skill data-security-compliance-and-regulated-datagit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-security-compliance-and-regulated-data)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-security-compliance-and-regulated-data"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-security-compliance-and-regulated-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-security-compliance-and-regulated-data"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-security-compliance-and-regulated-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.00861 |
| Opus 5 | $0.00028 | $0.00430 |
| Sonnet 5 | $0.00011 | $0.00172 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
data-security-compliance-and-regulated-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 93 lines · 55 tokens per session scan A a3d5327b84bc
data-security-compliance-and-regulated-data is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 861 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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