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Data Flow Mapping

Community
mukul975
data-flow-mapping

Guides systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.

Overview

Publishermukul975
RepositoryPrivacy-Data-Protection-Skills
Skill namedata-flow-mapping
Stars
279
Forks
59
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Data Flow Mapping AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git /tmp/Privacy-Data-Protection-Skills
mkdir -p .claude/skills
cp -r /tmp/Privacy-Data-Protection-Skills/plugins/cross-border-transfers-skills/skills/data-flow-mapping .claude/skills/data-flow-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Flow Mapping in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Flow Mapping on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Flow Mapping is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Mapping International Data Flows

Overview

A comprehensive international data flow map is the foundational prerequisite for any cross-border transfer compliance programme. GDPR Article 30 requires controllers and processors to document transfers to third countries or international organisations. Beyond regulatory compliance, a data flow inventory enables identification of unprotected transfers, assignment of appropriate transfer mechanisms, and ongoing monitoring of data movement across jurisdictions. This skill provides a structured methodology for conducting a system-by-system data flow inventory, identifying all third-party recipients, assigning transfer mechanisms, and performing gap analysis.

Data Flow Inventory Methodology

Phase 1: System Inventory

Identify every information system, application, and service that processes personal data within the organisation.

System categories at Athena Global Logistics:

CategorySystemsPersonal Data Processed
Enterprise Resource PlanningSAP S/4HANA (hosted Frankfurt DC)Employee data, customer data, supplier data, financial data
Transport ManagementCargoWise One (SaaS, hosted Sydney)Customer shipment data, consignee data, customs broker contacts
Customer Relationship ManagementSalesforce (SaaS, hosted Frankfurt)Customer contacts, communication history, sales pipeline
Human ResourcesWorkday (SaaS, hosted Dublin)Employee personal data, payroll, benefits, performance, recruitment
Email and CollaborationMicrosoft 365 (SaaS, hosted EU DC)Employee communications, contacts, calendar, file storage
Warehouse ManagementManhattan Associates (hosted Frankfurt DC)Warehouse worker IDs, shift schedules, access logs
Fleet ManagementFleetio (SaaS, hosted US)Driver names, licence numbers, GPS tracking data, vehicle assignments
Customer PortalCustom web application (hosted Frankfurt DC)Customer login credentials, shipment tracking, document uploads
Analytics PlatformSnowflake (SaaS, hosted Frankfurt)Aggregated operational data, pseudonymised customer analytics
IT Service ManagementServiceNow (SaaS, hosted Amsterdam)Employee IT tickets, contact details, device assignments

Phase 2: Data Flow Identification

For each system, trace every flow of personal data that crosses a national border.

Data flow tracing methodology:

  1. Inbound flows: Where does personal data enter the system from? (user input, API integrations, file imports, email)
  2. Internal processing: Where is data stored and processed? (primary data centre, disaster recovery site, development/test environments)
  3. Outbound flows: Where does personal data leave the system to? (third-party integrations, data exports, email transmissions, backup replication)
  4. Sub-processor chains: For SaaS systems, identify the provider's sub-processors and their locations.
  5. Support access: Identify any remote support arrangements where third-country support staff may access personal data.

Athena Global Logistics example data flow register (excerpt):

Flow IDSource SystemSource LocationDestinationDestination CountryData CategoriesLegal Basis for ProcessingTransfer Mechanism
DF-001SAP S/4HANAFrankfurt, DEAthena Logistics (HK) Ltd — local SAP instanceHong Kong SARCustomer names, addresses, consignment dataArt. 6(1)(b) contractSCCs Module 1
DF-002CargoWise OneSydney, AUAthena Global Logistics GmbH — API pullAustraliaShipment status, consignee detailsArt. 6(1)(b) contractEU adequacy decision (implied — no decision for AU; SCCs Module 2 required)
DF-003WorkdayDublin, IEWorkday Inc sub-processor (US backup DC)United StatesEmployee HR dataArt. 6(1)(b) employment contractEU-US DPF + SCCs backup
DF-004FleetioAtlanta, USN/A (primary processing in US)United StatesDriver names, licence numbers, GPS dataArt. 6(1)(f) legitimate interestEU-US DPF (verify certification)
DF-005SAP S/4HANAFrankfurt, DEAthena Freight Services India — SFTP batchIndiaEmployee data (payroll, benefits)Art. 6(1)(b) employment contractSCCs Module 1
DF-006Custom portalFrankfurt, DETransPacific Freight Solutions — APIHong Kong SARCustomer shipment dataArt. 6(1)(b) contractSCCs Module 2
DF-007Microsoft 365EU DCMicrosoft Corp sub-processors (global)Multiple (US, SG, IE)Employee emails, filesArt. 6(1)(b) contractEU-US DPF (US); SCCs (SG)

Phase 3: Third-Party Identification

Catalogue all third parties (processors, sub-processors, joint controllers, independent controllers) that receive personal data through international transfers.

Third PartyRoleCountryData ReceivedPurposeContract Reference
TransPacific Freight Solutions LtdProcessorHong Kong SARCustomer shipment dataFreight consolidation and customs clearanceDPA-2025-001
CloudVault Asia Pte LtdSub-processor (of TransPacific)SingaporeCustomer shipment data (hosting)Cloud infrastructureSub-processor agreement via TransPacific
Pinnacle Data Services Co LtdSub-processor (of TransPacific)ThailandCustoms documentation dataData entry and validationSub-processor agreement via TransPacific
Athena Freight Services India Pvt LtdController (intra-group)IndiaEmployee HR dataLocal employment administrationIntra-group DPA-2024-005
Workday IncProcessorIreland (primary), US (backup)Employee HR dataHRIS platformDPA-WD-2024-001
Fleetio IncProcessorUnited StatesDriver dataFleet managementDPA-FL-2024-003
Microsoft CorporationProcessorEU, US, SingaporeEmployee email and filesEmail and collaborationDPA-MS-2024-001

Phase 4: Transfer Mechanism Assignment

For each identified international data flow, assign the appropriate transfer mechanism:

For each flow:
  1. Check: Does the destination have an EU adequacy decision?
     → YES: Record "Adequacy Decision" as mechanism. Done.
     → NO: Continue.
  2. Check: Is the importer DPF-certified (for US transfers)?
     → YES: Record "EU-US DPF" as mechanism. Recommend SCCs as backup.
     → NO: Continue.
  3. Check: Are SCCs in place between the parties?
     → YES: Record "SCCs Module X" as mechanism. Verify TIA completed.
     → NO: Continue.
  4. Check: Are BCRs in place covering the transfer?
     → YES: Record "BCRs" as mechanism. Verify scope covers the data.
     → NO: Continue.
  5. Check: Does an Art. 49 derogation apply?
     → YES: Record "Art. 49(1)(x)" as mechanism. Document justification.
     → NO: FLAG AS UNPROTECTED TRANSFER — immediate action required.

Phase 5: Gap Analysis

Identify transfers that lack a valid transfer mechanism:

Gap TypeDescriptionPriorityRemediation
No mechanismTransfer occurring without any Art. 45/46/49 basisCriticalSuspend transfer or execute SCCs within 30 days
Expired mechanismSCCs based on superseded 2010/2021 versions; DPF certification expiredHighRenew mechanism within 60 days
Missing TIASCCs in place but no documented TIAHighComplete TIA within 30 days
Incomplete documentationMechanism exists but Annex fields are incompleteMediumComplete documentation within 60 days
Sub-processor gapImporter uses sub-processors not covered by SCCsHighExtend SCC coverage or require importer to execute Module 3 SCCs
Undiscovered flowData flow identified during mapping that was not previously knownHighAssess, assign mechanism, and document within 30 days

Data Flow Visualisation

Visualisation Approaches

  1. Geo-map visualisation: Plot data flows on a world map with colour-coded lines:

    • Green: Transfer covered by adequacy decision
    • Blue: Transfer covered by SCCs/BCRs with completed TIA
    • Yellow: Transfer covered by mechanism but TIA pending or in review
    • Red: Transfer lacking valid mechanism — immediate action required
  2. System-centric diagram: For each major system, draw a diagram showing all inbound and outbound data flows with destination countries and mechanisms.

  3. Third-party relationship map: Network diagram showing the organisation at the centre with all third parties and data flows radiating outward, grouped by jurisdiction.

  4. Transfer register dashboard: Tabular view with filtering by mechanism type, destination country, risk level, and review status.

Ongoing Maintenance

  1. Trigger-based updates: Re-map data flows upon: new system implementation, new vendor onboarding, corporate restructuring, new country operations, new data categories.
  2. Periodic review: Full data flow inventory review at least annually.
  3. Automated discovery: Implement network monitoring and data loss prevention (DLP) tools to detect undocumented cross-border data flows.
  4. Integration with RoPA: Data flow map feeds directly into the Art. 30 Records of Processing Activities.
  5. Integration with vendor register: Third-party data recipients map feeds into the vendor management and DPA tracking system.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Flow Mapping AI skill do?

Guides systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.

Why use Data Flow Mapping on TypingMind?

Because you install it once and use it with any model. Data Flow Mapping is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Flow Mapping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/plugins/cross-border-transfers-skills/skills/data-flow-mapping. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Flow Mapping?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Flow Mapping?

As many as you like. As long as a model supports skills, you can use Data Flow Mapping with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Flow Mapping AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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