Agent skill

Nist Pf Control

by mukul975 in mukul975/Privacy-Data-Protection-Skills

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing.

Apache-2.0Auto-check passedLegal & Compliance

Install Nist Pf Control

skills CLI
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill nist-pf-control -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Privacy-Data-Protection-Skills nist-pf-control --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/privacy/nist-pf-control .claude/skills/nist-pf-control && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nist-pf-control
GitHub stars
301
Token cost
~2.5k tokens
SKILL.md length
925 words
Files
5 (incl. scripts, references, assets)
Skills in repo
280
Repo updated
First seen
Licence
Apache-2.0

At a glance

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing.

  • Tasks that involve Privacy and GDPR
  • SKILL.md covers Overview, CONTROL Function Subcategories, Data Management Architecture and Disassociated Processing…, plus 4 more sections
  • Runs Python scripts from its folder

What it does

Nist Pf Control is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/standards.md` and `references/workflows.md`).

It sits in Legal & Compliance, covering Privacy and GDPR. The repository describes itself as: 282+ structured privacy & data protection skills for AI agents. GDPR, CCPA, EU AI Act, HIPAA, LGPD, PIPL, DPDP Act. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Privacy and GDPR

Example prompts

  • “/nist-pf-control”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9b2ef9e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nist Pf Control loads about 2.5k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 925 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Privacy-Data-Protection-Skills at commit 9b2ef9e, republished under its Apache-2.0 licence (© mukul975). 925 words, ~2,509 tokens.

Download SKILL.mdSave it as .claude/skills/nist-pf-control/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
nist-pf-control
description
Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.
license
Apache-2.0
metadata.author
mukul975
metadata.version
1.0
metadata.domain
privacy
metadata.subdomain
privacy-engineering
metadata.tags
nist-privacy-framework, control-function, data-management, disassociated-processing, de-identification

NIST Privacy Framework — CONTROL Function

Overview

The CONTROL function develops and implements appropriate activities to enable organizations and individuals to manage data with sufficient granularity to manage privacy risks. It addresses data management, data processing governance, and disassociated processing techniques.

CONTROL Function Subcategories

CT.DM — Data Management

Policies and procedures for managing data throughout its lifecycle.

SubcategoryDescriptionImplementation Guidance
CT.DM-P1Data elements can be accessed for reviewImplement data subject access portals. Provide export in machine-readable formats (JSON, CSV). Response within regulatory timelines.
CT.DM-P2Data elements can be accessed for transmission or disclosureSupport data portability requests. Implement secure transfer mechanisms. Maintain disclosure logs.
CT.DM-P3Data elements can be accessed for alterationEnable data correction workflows. Propagate corrections across all systems holding the data.
CT.DM-P4Data elements can be accessed for deletionImplement cascading deletion across primary and backup systems. Maintain deletion verification logs. Address data in analytics pipelines.
CT.DM-P5Data processing activities are configured to meet the organization's data processing principlesDefault to most privacy-protective settings. Implement purpose limitation at the system level.
CT.DM-P6Retention schedules are established and in placeDefine retention periods per data category. Implement automated purging. Document legal hold exceptions.
CT.DM-P7Data are processed in accordance with established retention schedulesAutomated enforcement of retention policies. Regular audits of data stores. Reporting on retention compliance.
CT.DM-P8Integrity of data elements is maintainedImplement checksums and validation rules. Detect and resolve data quality issues. Track data lineage.
CT.DM-P9Audit/log records are determined, documented, implemented, and reviewedPrivacy-specific audit logging for all data access and modifications. Log retention per regulatory requirements.
CT.DM-P10Mechanisms for data subjects to authorize processing existConsent collection and management infrastructure. Granular consent options. Consent withdrawal capability.
CT.DP — Data Processing Policies and Procedures

Governance controls for data processing activities.

SubcategoryDescriptionImplementation Guidance
CT.DP-P1Policies, processes, and procedures for authorizing data processing are maintained and enforcedDocument lawful basis for each processing activity. Implement access controls aligned with authorized purposes.
CT.DP-P2Data processing environment is identified, managed, and monitoredMaintain environment inventory (production, staging, development). Monitor for unauthorized processing. Separate test data from production data.
CT.DP-P3Data processing procedures are maintained and monitored to meet established termsStandard operating procedures for each processing type. Automated compliance checking. Exception management workflow.
CT.DP-P4System or device configurations are set to authorized specificationsSecure configuration baselines for systems processing personal data. Configuration drift detection. Change management for privacy-relevant settings.
CT.DP-P5Data processing activities are monitored and loggedReal-time monitoring of data processing operations. Anomaly detection for unusual processing patterns. Alerting for policy violations.
CT.PO — Disassociated Processing

Techniques to process data while minimizing identifiability.

SubcategoryDescriptionImplementation Guidance
CT.PO-P1De-identification is applied to data elements to limit observability or linkabilityApply pseudonymization, anonymization, or generalization based on use case. Document de-identification methodology.
CT.PO-P2Technical measures for disassociated processing are implementedDeploy differential privacy, secure multi-party computation, or homomorphic encryption as appropriate. Select technique based on data utility requirements.

Data Management Architecture

Lifecycle Control Points
Collection --> Storage --> Processing --> Sharing --> Retention --> Deletion
    |            |           |             |            |            |
    v            v           v             v            v            v
 Consent     Encryption   Purpose      Access       Schedule    Cascading
 Capture     at Rest      Limitation   Controls     Enforcement  Purge
    |            |           |             |            |            |
    v            v           v             v            v            v
 Audit Log   Access Log   Process Log  Transfer Log Retention Log Deletion Log
Data Element Access Control Matrix
OperationData SubjectData StewardProcessorAnalystAdmin
View own dataYesYesNoNoYes
Export own dataYesYesNoNoYes
Correct own dataRequestYesNoNoYes
Delete own dataRequestYesNoNoYes
View aggregateNoYesYesYesYes
Process dataNoNoYesNoYes
Analyze dataNoNoNoYesYes
Configure systemNoNoNoNoYes
Show full SKILL.md (356 more words)Show less

Disassociated Processing Techniques

Technique Selection Guide
TechniqueIdentifiability ReductionData UtilityComputational CostReversible
PseudonymizationMediumHighLowYes (with key)
k-AnonymityMedium-HighMediumMediumNo
l-DiversityHighMediumMediumNo
t-ClosenessHighMedium-LowHighNo
Differential PrivacyVery HighMediumMediumNo
Data MaskingLow-MediumMediumLowDepends
GeneralizationMediumMediumLowNo
SuppressionHighLowLowNo
Synthetic DataVery HighVariableHighNo
De-identification Decision Framework
Is the data being shared externally?
├── Yes → Is re-identification risk acceptable?
│   ├── No → Apply formal anonymization (differential privacy, k-anonymity with k>=5)
│   └── Yes → Apply pseudonymization with contractual controls
└── No → Is the data used for analytics?
    ├── Yes → Apply aggregation or differential privacy
    └── No → Apply pseudonymization with access controls

Retention Schedule Template

Data CategoryRetention PeriodLegal BasisDeletion MethodVerification
Customer transaction records7 years from transactionTax/accounting regulationsSecure overwriteAutomated audit
Marketing consent recordsDuration of consent + 3 yearsLegitimate interest (proof)Logical deletionAnnual review
Employee personnel filesEmployment + 7 yearsEmployment lawSecure destructionHR verification
Website analytics26 monthsConsentAutomated purgeMonthly check
Support tickets3 years from resolutionContract performanceSecure overwriteQuarterly audit
CCTV footage30 daysLegitimate interestAutomated overwriteWeekly check
Backup tapes90 days rollingBusiness continuityTape degaussingQuarterly audit

Audit Logging Requirements

Privacy-Specific Events to Log
Event CategorySpecific EventsRequired Fields
Data AccessView, export, downloadUser ID, timestamp, data subject ID, data elements accessed, purpose
Data ModificationCreate, update, correctUser ID, timestamp, data subject ID, before/after values, purpose
Data DeletionDelete, purge, anonymizeUser ID, timestamp, data subject ID, deletion scope, verification
Consent ChangesGrant, withdraw, modifyData subject ID, timestamp, consent scope, collection channel
Data TransferInternal transfer, external disclosureUser ID, timestamp, recipient, data elements, legal basis
ConfigurationAccess control changes, retention policy changesAdmin ID, timestamp, before/after settings, approval reference

Control Mapping

NIST PF CONTROLISO 27701GDPR ArticleNIST 800-53
CT.DM-P1A.7.3.3Art. 15PM-25
CT.DM-P2A.7.3.7Art. 20PM-25
CT.DM-P3A.7.3.4Art. 16PM-25
CT.DM-P4A.7.3.5Art. 17PM-25
CT.DM-P6A.7.4.7Art. 5(1)(e)SI-12
CT.DM-P9A.7.2.8Art. 30AU-2
CT.DM-P10A.7.2.3Art. 6, 7IP-1
CT.DP-P1A.7.2.2Art. 6(1)PT-3
CT.DP-P5A.7.2.8Art. 30AU-6
CT.PO-P1A.7.4.5Art. 25(1)PM-24
CT.PO-P2A.7.4.5Art. 25(1)PM-24

References

  • NIST Privacy Framework Version 1.0 (January 16, 2020)
  • NIST SP 800-188 — De-Identifying Government Datasets
  • NIST IR 8053 — De-Identification of Personal Information
  • ISO/IEC 20889:2018 — Privacy Enhancing Data De-identification Techniques

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references, assets) in skills/privacy/nist-pf-control of mukul975/Privacy-Data-Protection-Skills.

  • SKILL.md
  • assets/template.md
  • references/standards.md
  • references/workflows.md
  • scripts/process.py

Open the folder on GitHubat commit 9b2ef9e

Compare with similar skills

Nist Pf Control next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Nist Pf Control compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nist Pf Control this skillmukul975/Privacy-Data-Protection-Skills301—~2.5kAutomated safety check: PassApache-2.0
C15tc15t/c15t1.9k1 repos~1.6kAutomated safety check: PassApache-2.0
HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed5.5k—~1.7kAutomated safety check: PassApache-2.0
Korean Privacy Termskimlawtech/korean-privacy-terms587—~2.9kAutomated safety check: PassApache-2.0
Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~3.9kAutomated safety check: PassMIT
Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~2.3kAutomated safety check: PassMIT

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Questions about Nist Pf Control

What does Nist Pf Control do?

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Nist Pf Control is an agent skill from mukul975/Privacy-Data-Protection-Skills.PO disassociated processing.

When should I use Nist Pf Control?

Nist Pf Control fits situations like: tasks that involve Privacy and GDPR.

How do I install Nist Pf Control in Claude Code?

Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill nist-pf-control -a claude-code`. Or copy the skill folder (skills/privacy/nist-pf-control in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/nist-pf-control in your project. Claude Code loads it when a task matches its description.

How do I install Nist Pf Control in Codex?

Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill nist-pf-control -a codex`. Or copy the skill folder (skills/privacy/nist-pf-control in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/nist-pf-control in your project. Codex loads it when a task matches its description.

Can I use Nist Pf Control in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill nist-pf-control -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nist-pf-control, .gemini/skills/nist-pf-control, .github/skills/nist-pf-control and .opencode/skills/nist-pf-control in your project.

What does Nist Pf Control need to run?

Going by SKILL.md and its folder, Nist Pf Control needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Nist Pf Control access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nist Pf Control safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Nist Pf Control use?

Nist Pf Control is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nist Pf Control use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 791 tokens, read only when the agent opens those files.

What are the alternatives to Nist Pf Control?

Skills that share tags, products or a category with Nist Pf Control: C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Korean Privacy Terms (kimlawtech/korean-privacy-terms, 587 stars) and Gdpr Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nist Pf Control?

mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 301 GitHub stars. The repository holds 280 skills in this directory. The repository was last updated on March 16, 2026.

Source: mukul975/Privacy-Data-Protection-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.