privacy-policy-mkurman

privacy-policy-mkurman is a skill for Claude Code, Codex from ThomasMoreAI/legal-skills-open. It costs 34 tokens per session (1,808 once invoked), scanned A, a copy of privacy-policy, Apache-2.0.

A privacy-policy assistant for creating, reviewing, or updating product privacy documents. It considers the product, markets, data collected, third-party services, and privacy laws that may apply.

In plain words
What is it for?
Use it for new-product launches, privacy-policy updates, expansion into jurisdictions such as the EU or California, and reviews of current data handling.
Why use it?
It helps keep the policy aligned with changes such as a new feature, service provider, or country launch. It also prompts teams to collect the business and technical details needed for an accurate disclosure.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for new-product launches, privacy-policy updates, expansion into jurisdictions such as the EU or California, and reviews of current data handling.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman
Install

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.

Any agent
npx skills add ThomasMoreAI/legal-skills-open --skill privacy-policy-mkurman
Clone the repo
git clone --depth 1 https://github.com/ThomasMoreAI/legal-skills-open

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for privacy-policy-mkurman

README.md
[![agentmods](https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman/github.svg)](https://agentmods.dev/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman)
Your own site
<a href="https://agentmods.dev/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman/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.

agentmods 80×15 button for privacy-policy-mkurman

Your own site · 80×15
<a href="https://agentmods.dev/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/privacy-policy-mkurman.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,808 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.01808
Opus 5 $0.00017 $0.00904
Sonnet 5 $0.00007 $0.00362
Haiku 4.5 $0.00003 $0.00181

Measured 9d ago against content hash e9395fc838a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

privacy-policy-mkurman 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.

Origin

This is a copy

91% identical to privacy-policy — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

cross-jurisdiction/data-protection/skills/privacy-policy-mkurman/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Privacy Policy

When to Use

Activate when a founder needs to create a privacy policy for a new product launch, update an existing policy for new data practices or features, expand into a new jurisdiction (EU, California, etc.), or assess whether current data handling is properly disclosed. Also activate when the user asks about GDPR, CCPA, CPRA, or general data privacy compliance.

Context Required

  • From startup-context: product type, platform (web/mobile/API), target customer segments, geographic markets, business model, tech stack.
  • From the user: product name and URL, company legal name and address, contact email for privacy inquiries, what personal data is collected and how, which third-party services process data (analytics, payment processors, CRMs, AI providers), applicable jurisdictions, whether the product targets minors, and any existing privacy documentation.

Workflow

  1. Research the product -- Visit the product website or review the product description. Identify all data collection methods, third-party integrations, and primary features that involve personal data.
  2. Map data collection -- Categorize all data into: directly provided (forms, account creation), automatically collected (cookies, device info, usage data, IP addresses), third-party sources, and special/sensitive categories. Build a structured data inventory.
  3. Identify applicable laws -- Based on where users are located and where the company operates, determine which privacy frameworks apply: GDPR, CCPA/CPRA, state privacy laws, COPPA, industry-specific regulations. Note specific obligations per jurisdiction.
  4. Structure the policy -- Organize using the 15-section template below. Write in plain language at an 8th-grade reading level. Be specific about actual practices -- say "We collect your email address when you sign up" rather than "We may process identifiers."
  5. Flag legal review areas -- Mark sections requiring attorney review with [LEGAL REVIEW REQUIRED] notation. These include legal basis determinations, international transfer mechanisms, and jurisdiction-specific rights.
  6. Provide implementation context -- Explain why each section matters, what company decisions are needed, and what compliance considerations apply. Include a pre-publication checklist.
  7. Generate compliance summary -- Produce a separate document with data inventory table, jurisdiction applicability matrix, risk flags, and implementation checklist.

Read the full file on GitHub · 123 lines

Changes

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.

  1. 9d ago First seen · 123 lines · 34 tokens per session scan A e9395fc838a7

Subscribe to this mod's changes

privacy-policy-mkurman is a skill published in the GitHub repository ThomasMoreAI/legal-skills-open (72 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,808 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to privacy-policy, differing in 14 lines, and is treated as a copy.