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 05-deepak-patidar/claude-skills --skill data-privacy-compliancegit clone --depth 1 https://github.com/05-deepak-patidar/claude-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/05-deepak-patidar/claude-skills/data-privacy-compliance)<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/data-privacy-compliance"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/data-privacy-compliance/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/05-deepak-patidar/claude-skills/data-privacy-compliance"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/data-privacy-compliance.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 | $0.00114 | $0.01442 |
| Opus 5 | $0.00057 | $0.00721 |
| Sonnet 5 | $0.00023 | $0.00288 |
| Haiku 4.5 | $0.00011 | $0.00144 |
Grade A, and why
data-privacy-compliance 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 12d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Privacy Compliance
Personal data is not an asset with a downside; it's a liability with a use case. Every mobile number you store can leak, be subpoenaed, be demanded back by its owner, or trigger a regulator. The engineering stance: collect the minimum, know where every copy lives, and be able to prove deletion. Retrofitting privacy is 10× the cost of designing it in — this skill is the design-it-in checklist.
(Legal disclaimer that is itself a rule: this skill makes you ready for compliance; the final word on GDPR/DPDP/sector rules for your product comes from qualified counsel. Flag to the user when their situation needs one — payments, health, minors, cross-border.)
Gate 1: Minimization — the cheapest compliance is data you don't have
- For every field collected: what feature needs it? No feature, no field. "Might be useful later" is how liability accumulates. Optional fields default to not-asked, not merely not-required.
- Collect at the moment of need, not at signup ("we'll need GSTIN eventually" → ask when they create their first tax invoice).
- Derived beats raw: store age-band not birthdate, city not GPS trail, "verified: yes" not the ID document — whenever the feature allows.
- Third-party sprawl counts: every analytics snippet, error tracker, and marketing pixel is you sharing personal data. Each gets the question: what do they receive, is it configured to mask it (IP anonymization, PII scrubbing in the error tracker), and is there an agreement (DPA) behind it? Keep the list of processors written down — every privacy law asks for it.
Gate 2: The data map — you can't protect what you can't find
Maintain a one-page inventory: each category of personal data × where it lives — primary DB tables, logs, error tracker, analytics, backups, exports, search indexes, caches, third parties, spreadsheets someone downloaded. This map is the prerequisite for every other gate, and it's what deletion requests get executed against. Update it when schemas change (same-PR rule, docs-and-runbooks).
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.
- 12d ago First seen · 57 lines · 114 tokens per session scan A 67edcacbb7a4
data-privacy-compliance is a skill published in the GitHub repository 05-deepak-patidar/claude-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 114 tokens to every session and 1,442 once invoked, about $0.0006 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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