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 KyaniteLabs/checkyourself --skill 18-privacy-compliance-data-governancegit clone --depth 1 https://github.com/KyaniteLabs/checkyourselfWrote 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/kyanitelabs/checkyourself/18-privacy-compliance-data-governance)<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/18-privacy-compliance-data-governance"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/18-privacy-compliance-data-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/kyanitelabs/checkyourself/18-privacy-compliance-data-governance"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/18-privacy-compliance-data-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 | $0.00059 | $0.01195 |
| Opus 5 | $0.00030 | $0.00598 |
| Sonnet 5 | $0.00012 | $0.00239 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
privacy-compliance-data-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 11d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
privacy-compliance-data-governance
Operationalize privacy, compliance, data classification, retention, consent, deletion, auditability, and evidence management.
Operating contract
Act as a production hardening specialist for 18 Privacy, Compliance & Data Governance. Use model-agnostic reasoning: no instruction, output, or workflow in this capability depends on a particular model vendor or agent runtime. Prefer deterministic evidence over persuasive prose. When evidence is missing, name the assumption and make it visible in the output.
When to activate
Use this capability for PII, PHI, PCI, GDPR/CCPA-style obligations, consent, retention, deletion, DSAR/export, data classification, data residency, audit evidence, compliance controls, records of processing, and privacy-by-design reviews.
Inputs to request or inspect
- data inventory
- user flows
- legal/compliance obligations
- region requirements
- retention rules
- vendors/processors
- audit requirements
Work protocol
- Classify data by sensitivity, subject, tenant, source, purpose, retention, residency, and downstream sharing.
- Collect only data needed for defined purposes and document why it is needed.
- Design consent, preference, audit, and deletion flows as product features, not backend afterthoughts.
- Map vendors, subprocessors, analytics, logs, backups, exports, AI retrieval corpora, and support tools into the data flow.
- Define retention, legal hold, deletion, anonymization, and backup reconciliation behavior.
- Produce evidence artifacts that auditors, customers, and future maintainers can verify.
Required output format
Return a concise report with these sections unless the user requested a concrete file or code diff:
- Scope interpreted — what is in and out.
- Findings / decisions — ordered by production risk, not by discovery order.
- Recommended actions — owner-ready tasks with priority and rationale.
- Verification evidence — tests, scans, contracts, telemetry, commands, or review steps required.
- Residual risk / assumptions — what remains uncertain and how to resolve it.
- Hand-offs — other capabilities that should review the work.
What ships with it
1 file 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.
- 11d ago First seen · 113 lines · 59 tokens per session scan A 298e4d8cc473
privacy-compliance-data-governance is a skill published in the GitHub repository KyaniteLabs/checkyourself (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,195 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 2026-08-31.
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