Claude Skill

privacy-and-data-protection

Assesses and improves how personal data is collected, used, shared, and retained — data mapping, lawful basis, consent, processor agreements, subject rights, and breach obligations. Use this before launching anything that handles personal data, when adding a vendor that will proc

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Download cbrock84-headcount-plugins_legal-risk_skills_privacy-and-data-protection-98d1c17.zip · 2 KB
Part of cbrock84/headcount — 160 skills

Install

skills CLI npx skills add https://github.com/cbrock84/headcount/tree/main/plugins/legal-risk/skills/privacy-and-data-protection
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install cbrock84-headcount@llmmart
Git git clone https://github.com/cbrock84/headcount.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole cbrock84/headcount collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Privacy and data protection

Not legal advice. Regimes differ by jurisdiction and change; material questions need qualified counsel. This structures the assessment and identifies what to escalate.

Start from the data map

You cannot assess what you have not inventoried. For each category of personal data:

  • What is collected, from whom, and where it came from.
  • Why — the specific purpose, and the lawful basis where one is required.
  • Where it lives, who can reach it, and which vendors receive it.
  • How long it is kept, and what deletes it. "Indefinitely" is a finding, not an answer.
  • Whether it crosses a border, and under what mechanism.

Most privacy failures are inventory failures: data nobody remembered was being collected, in a system nobody owned.

Design decisions that prevent problems

  • Collect less. Every field is a liability with a maintenance cost. The cheapest way to protect data is not to hold it.
  • Purpose limitation is real. Data collected for one purpose is not automatically available for another — particularly for training models, which is where this most often goes wrong now.
  • Separate identifiers from behavior where analysis does not require linkage.
  • Retention with an enforcing mechanism. A policy with no deletion job is a statement of intent.

Consent, where it applies

Specific, informed, freely given, and as easy to withdraw as to give. Pre-ticked boxes, bundled consent, and cookie walls that offer no genuine choice fail on their face in the regimes that require consent.

Note that consent is one lawful basis among several and often the weakest — it can be withdrawn, and then the processing must stop.

Vendors

Any third party processing personal data on your behalf needs a written agreement covering purpose, security, sub-processors, deletion, and assistance with subject rights. Sending data to a vendor without one is a common and easily avoided violation.

Assess the vendor's actual security, not their questionnaire answers, in proportion to the sensitivity of what they will hold.

Subject rights and breaches

Have a working process before the first request: how it arrives, how identity is verified, how the data is located across systems, and the deadline. Locating the data is the part that fails.

For breaches, know your notification clock before you need it — several regimes measure it in hours from awareness. Decide in advance who determines that awareness has occurred.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Collect data because it may be useful later. Purpose first, then collection.
  • Retain personal data past the period you published.
  • Send personal data to a vendor before the contract terms and the transfer basis are in place.
  • Load production personal data into a test environment.
Files (headcount)
  • references
    • sources.md 1.7 KB
      # Sources — `legal-risk:privacy-and-data-protection`
      
      <!-- Generated by scripts/build-sources.py from sources/*.toml. Do not edit. -->
      
      Check these before answering on anything they cover, and cite what you used. The use note on each one is binding: most of what a professional cites is free to read and not free to reproduce.
      
      ## California Consumer Privacy Act — Attorney General
      
      California Office of the Attorney General · US-CA · public domain — quote freely
      
      <https://oag.ca.gov/privacy/ccpa>
      
      **Authoritative for:** The largest US state privacy regime, and the template several other states followed. Start here before generalizing about 'US state privacy law', which is not one thing.
      
      ## European Data Protection Board guidelines and recommendations
      
      EDPB · EU · free to use with attribution — credit the publisher
      
      <https://www.edpb.europa.eu/our-work-tools/general-guidance/guidelines-recommendations-best-practices_en>
      
      **Authoritative for:** How supervisory authorities actually read the GDPR — lawful basis, transfers, legitimate interest balancing — which is where a plain reading of the regulation stops being enough.
      
      ## Regulation (EU) 2016/679 — GDPR
      
      Publications Office of the European Union · EU · free to use with attribution — credit the publisher
      
      <https://eur-lex.europa.eu/eli/reg/2016/679/oj>
      
      **Authoritative for:** The operative text of the GDPR. EU legal texts are reusable with attribution under the Commission's reuse decision, so this is quotable where a national summary is not.
      
      ---
      
      Sources are maintained in `sources/` upstream, not here. If one is wrong, out of date, or missing, fix it there — this file is regenerated and an edit to it is lost.
      
  • SKILL.md 3.5 KB
    ---
    name: privacy-and-data-protection
    description: Assesses and improves how personal data is collected, used, shared, and retained — data mapping, lawful basis, consent, processor agreements, subject rights, and breach obligations. Use this before launching anything that handles personal data, when adding a vendor that will process it, when a data subject request arrives, when assessing exposure under GDPR or US state privacy laws, or when preparing for a privacy review.
    ---
    
    # Privacy and data protection
    
    > Not legal advice. Regimes differ by jurisdiction and change; material questions need qualified
    > counsel. This structures the assessment and identifies what to escalate.
    
    ## Start from the data map
    
    You cannot assess what you have not inventoried. For each category of personal data:
    
    - What is collected, from whom, and where it came from.
    - Why — the specific purpose, and the lawful basis where one is required.
    - Where it lives, who can reach it, and which vendors receive it.
    - How long it is kept, and what deletes it. "Indefinitely" is a finding, not an answer.
    - Whether it crosses a border, and under what mechanism.
    
    Most privacy failures are inventory failures: data nobody remembered was being collected, in a
    system nobody owned.
    
    ## Design decisions that prevent problems
    
    - **Collect less.** Every field is a liability with a maintenance cost. The cheapest way to protect
      data is not to hold it.
    - **Purpose limitation is real.** Data collected for one purpose is not automatically available for
      another — particularly for training models, which is where this most often goes wrong now.
    - **Separate identifiers from behavior** where analysis does not require linkage.
    - **Retention with an enforcing mechanism.** A policy with no deletion job is a statement of intent.
    
    ## Consent, where it applies
    
    Specific, informed, freely given, and as easy to withdraw as to give. Pre-ticked boxes, bundled
    consent, and cookie walls that offer no genuine choice fail on their face in the regimes that
    require consent.
    
    Note that consent is one lawful basis among several and often the weakest — it can be withdrawn,
    and then the processing must stop.
    
    ## Vendors
    
    Any third party processing personal data on your behalf needs a written agreement covering purpose,
    security, sub-processors, deletion, and assistance with subject rights. Sending data to a vendor
    without one is a common and easily avoided violation.
    
    Assess the vendor's actual security, not their questionnaire answers, in proportion to the
    sensitivity of what they will hold.
    
    ## Subject rights and breaches
    
    Have a working process before the first request: how it arrives, how identity is verified, how the
    data is located across systems, and the deadline. Locating the data is the part that fails.
    
    For breaches, know your notification clock before you need it — several regimes measure it in hours
    from awareness. Decide in advance who determines that awareness has occurred.
    
    ## Sources
    
    `references/sources.md` in this skill lists the outside authorities that settle the questions
    here — what each one is authoritative for, and what you may do with it. Check them before
    answering on anything they cover, and cite what you used. Most are free to read and not free
    to reproduce; the use note on each is binding.
    
    ## Never
    
    - Collect data because it may be useful later. Purpose first, then collection.
    - Retain personal data past the period you published.
    - Send personal data to a vendor before the contract terms and the transfer basis are in place.
    - Load production personal data into a test environment.
    

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