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 lineage-pii-and-governancegit 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/lineage-pii-and-governance)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/lineage-pii-and-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lineage-pii-and-governance/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/lineage-pii-and-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lineage-pii-and-governance.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 | $0.00041 | $0.00485 |
| Opus 5 | $0.00020 | $0.00243 |
| Sonnet 5 | $0.00008 | $0.00097 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
lineage-pii-and-governance 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lineage, PII, And Governance
Overview
Governance is an engineering concern, not a cleanup exercise. This skill ensures that every meaningful data change accounts for ownership, lineage, access, and sensitive-data handling before release.
When to Use
- publishing a new table, model, or stream
- changing business-critical metrics
- handling personal, financial, health, or otherwise sensitive data
- modifying access controls or data-sharing patterns
- changing upstream or downstream lineage
Workflow
-
Identify ownership and consumers. Every published dataset should have:
- an owner
- intended consumers
- known downstream dependencies
-
Classify the data. Determine whether fields are:
- public
- internal
- confidential
- regulated or sensitive
-
Define required controls. Controls may include:
- masking
- tokenization
- row-level restrictions
- column-level restrictions
- encryption requirements
- retention or deletion rules
-
Update lineage and documentation. Record how the data flows from source to publish layer, including major transformations.
-
Verify policy enforcement in implementation. Do not stop at documentation. Check that access and masking rules are actually reflected in code or platform configuration.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It is only internal data." | Internal datasets still create exposure, misuse, and compliance risk. |
| "We will document lineage later." | Lineage that is not updated during change work becomes stale immediately. |
| "Security will handle masking downstream." | Sensitive data should be controlled as close to production as possible. |
Red Flags
- published data has no named owner
- sensitive fields are copied without classification
- lineage updates are missing for a shared metric
- access rules are assumed but not enforced
Verification
- Dataset ownership and consumers are identified
- Sensitive fields are classified
- Required controls are implemented or explicitly planned
- Lineage and documentation reflect the change
- Governance checks are based on real enforcement, not comments alone
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 72 lines · 41 tokens per session scan A f20850d53d84
lineage-pii-and-governance is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 485 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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