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 privacy-retention-and-right-to-deletegit 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/privacy-retention-and-right-to-delete)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/privacy-retention-and-right-to-delete"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/privacy-retention-and-right-to-delete/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/privacy-retention-and-right-to-delete"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/privacy-retention-and-right-to-delete.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 | $0.00049 | $0.00487 |
| Opus 5 | $0.00024 | $0.00244 |
| Sonnet 5 | $0.00010 | $0.00097 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
privacy-retention-and-right-to-delete 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 9d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy, Retention, And Right To Delete
Overview
Use this skill when the data platform must respect privacy obligations as an engineering behavior, not a policy slide. It helps agents make retention, deletion, masking, and minimization explicit across storage, transformations, and publishes.
When to Use
- handling personal or regulated data
- defining retention rules
- implementing deletion or erasure requests
- changing how sensitive fields are stored, copied, or published
- validating that downstream systems do not retain data longer than allowed
Do not assume masking alone satisfies retention or deletion obligations.
Workflow
-
Classify the data and obligations. Clarify:
- sensitive fields
- retention limit
- deletion trigger
- legal hold exceptions
- downstream replication paths
-
Map where the data lives. Include:
- raw landing
- transformed tables
- serving layers
- extracts
- caches and feature stores
-
Define the enforcement path. Decide how the system will:
- prevent unnecessary copies
- enforce retention windows
- process deletions
- prove compliance actions happened
-
Validate downstream propagation. Deletion in one layer is not enough if copies remain elsewhere.
-
Record exceptions and audit evidence.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The source system already deleted it." | Downstream data products may still retain copies. |
| "We only use hashed identifiers." | Hashing does not eliminate all privacy or retention duties. |
| "We can clean up old data later." | Retention failures often become expensive compliance incidents. |
Red Flags
- no retention schedule exists
- deletion requests stop at one system boundary
- old extracts and caches are ignored
- privacy controls rely on undocumented manual steps
Verification
- Sensitive data and retention obligations are classified
- All material storage locations and copies are mapped
- Deletion and retention enforcement are explicit and testable
- Audit evidence or runbooks exist for compliance actions
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.
- 9d ago First seen · 73 lines · 49 tokens per session scan A e32baf60b9f5
privacy-retention-and-right-to-delete is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 487 once invoked, about $0.0002 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 2026-09-03.
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