System Design
openxlings/xlings
Design systems, services, and architectures. An agent skill from openxlings/xlings.
Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas.
Install Automated Ropa Generation
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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/automated-ropa-generation .claude/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.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/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .claude/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/privacy/automated-ropa-generation .agents/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .agents/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/privacy/automated-ropa-generation .cursor/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .cursor/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Privacy-Data-Protection-Skills.git --path skills/privacy/automated-ropa-generation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/privacy/automated-ropa-generation .gemini/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .gemini/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/privacy/automated-ropa-generation .github/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .github/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills automated-ropa-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/privacy/automated-ropa-generation .opencode/skills/automated-ropa-generation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "automated-ropa-generation" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/automated-ropa-generation into .opencode/skills/automated-ropa-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "automated-ropa-generation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
automated-ropa-generation- GitHub stars
- 301
- Token cost
- ~3.7k tokens
- SKILL.md length
- 1,142 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas.
- Works in 4 steps: Connect to each database identified in… → Extract table schemas (column names,… → Apply personal data identification… → …
- Tasks that involve Microservices
- SKILL.md covers Overview, Data Sources for Automated…, Automated Field Population Logic and Automation Coverage Summary, plus 2 more sections
- Runs Python scripts from its folder
What it does
Automated Ropa Generation is an agent skill from mukul975/Privacy-Data-Protection-Skills. Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven records.
Its SKILL.md is about 3.7k 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 Databases, covering Microservices, Database schema design and Red teaming and adversary simulation. 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 Microservices
- Tasks that involve Database schema design
- Tasks that involve Red teaming and adversary simulation
Example prompts
- “Use the automated-ropa-generation skill to generate Records of Processing Activities automatically from IT system inventories including Active…”
- “/automated-ropa-generation”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Connect to each database identified in the cloud catalog.
- Extract table schemas (column names, data types, constraints).
- Apply personal data identification patterns to column names.
- Map identified personal data columns to Art. 30(1)(c) data categories.
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
Automated Ropa Generation loads about 3.7k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 1,142 words of instructions outside code blocks.
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). 1,142 words, ~3,687 tokens.
Download SKILL.mdSave it as
.claude/skills/automated-ropa-generation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- automated-ropa-generation
- description
- Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven records.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- records-of-processing
- metadata.tags
- gdpr, ropa, automation, data-discovery, active-directory, cloud-catalog, api-gateway, schema
Automated RoPA Generation
Overview
Manual RoPA creation through interviews and questionnaires is time-consuming, subjective, and prone to omitting processing activities that stakeholders forget or are unaware of. Automated RoPA generation leverages existing IT system inventories, cloud service catalogs, API gateway logs, and database schemas to discover processing activities and pre-populate Art. 30(1) fields. This approach reduces the RoPA creation burden by 60-80% while improving coverage, as it captures processing activities that manual interviews routinely miss (shadow IT, automated data pipelines, third-party integrations).
Data Sources for Automated RoPA Population
Source 1: Active Directory / Azure AD
What it reveals:
- Organisational structure (departments, teams) for mapping processing owners
- Group memberships revealing who has access to what systems (recipient categories)
- Application registrations and service principals (processing system inventory)
- Conditional access policies (security measures documentation)
Art. 30 fields populated:
| Field | Data Extracted from AD |
|---|---|
| Art. 30(1)(a) — Controller identity | Organisation name, department hierarchy, DPO group membership |
| Art. 30(1)(d) — Recipients | Security group memberships reveal internal access patterns |
| Art. 30(1)(g) — Security measures | Conditional access policies, MFA enforcement status |
Discovery approach for Helix Biotech Solutions:
Azure AD Tenant: helix-biotech.onmicrosoft.com
├── Enterprise Applications (87 registered)
│ ├── SAP SuccessFactors → HR processing activities
│ ├── Veeva Vault CDMS → Clinical trial data management
│ ├── Salesforce → Customer relationship management
│ ├── Google Analytics → Website analytics
│ ├── ADP Workforce Now → Payroll processing
│ └── ... (82 more applications)
├── Security Groups (134 groups)
│ ├── SG-HR-Payroll (4 members) → Access to payroll data
│ ├── SG-Clinical-Data (12 members) → Access to clinical trial data
│ ├── SG-Finance-AP (6 members) → Access to accounts payable
│ └── ... (131 more groups)
└── Conditional Access Policies (23 policies)
├── Require MFA for all users
├── Block legacy authentication
├── Require compliant device for clinical systems
└── ... (20 more policies)Source 2: Cloud Service Catalog
What it reveals:
- All cloud services (IaaS, PaaS, SaaS) in use
- Data residency (which regions/countries data is stored in)
- Service provider identity (processors and sub-processors)
- Data classification tags (if implemented)
AWS example for Helix Biotech Solutions:
| AWS Service | Region | Personal Data Indicator | Mapped Processing Activity |
|---|---|---|---|
| RDS (PostgreSQL) | eu-central-1 | Tagged: data-classification=confidential | Employee HR database |
| S3 Bucket: helix-clinical-data | eu-central-1 | Tagged: data-classification=restricted, contains-special-category=true | Clinical trial document storage |
| S3 Bucket: helix-marketing | eu-central-1 | Tagged: data-classification=internal | Marketing analytics data |
| Lambda: payroll-processor | eu-central-1 | Invoked by ADP integration | Payroll data transformation |
| CloudFront Distribution | Global (edge) | Logs contain IP addresses | Website content delivery |
| SES (Simple Email Service) | eu-west-1 | Sends to email addresses | Customer and employee email communications |
Azure example:
| Azure Service | Region | Data Classification | Mapped Processing Activity |
|---|---|---|---|
| Azure SQL | West Europe | Confidential | Finance and accounting database |
| Azure Blob Storage | West Europe | Restricted | Clinical trial imaging data |
| Azure AD | Global | Internal | Identity and access management |
| Azure Monitor | West Europe | Internal | System monitoring (may capture user activity) |
Source 3: API Gateway Logs
What it reveals:
- Which APIs handle personal data (based on request/response inspection)
- Data flow patterns between systems (upstream and downstream)
- Third-party API integrations (external recipients)
- Volume and frequency of data processing (helps assess whether processing is "occasional" for Art. 30(5))
API Gateway analysis for Helix Biotech Solutions:
| API Endpoint | Method | Personal Data Fields | Upstream System | Downstream System | Daily Volume |
|---|---|---|---|---|---|
| /api/v2/employees | GET/POST | employee_id, name, email, department | SAP SuccessFactors | Internal HR portal | 340 requests |
| /api/v2/patients/{id}/records | GET | patient_id, diagnosis, treatment | Veeva Vault CDMS | Clinical reporting dashboard | 1,200 requests |
| /api/v2/orders | POST | customer_name, email, billing_address | Salesforce | SAP ERP | 890 requests |
| /api/v1/analytics/events | POST | ip_address, user_agent, page_url | Website (JS tracker) | Google Analytics | 45,000 requests |
| /api/v2/vendor/payments | POST | vendor_name, bank_account, tax_id | SAP ERP | Banking API (Deutsche Bank) | 120 requests |
Source 4: Database Schemas
What it reveals:
- Exact data categories stored (column names and types)
- Table relationships revealing data flows
- Personal data identification through column name pattern matching
- Data volume and growth patterns
Schema analysis approach:
- Connect to each database identified in the cloud catalog.
- Extract table schemas (column names, data types, constraints).
- Apply personal data identification patterns to column names.
- Map identified personal data columns to Art. 30(1)(c) data categories.
Personal data column name patterns:
| Pattern | Data Category | Art. 9 Special Category |
|---|---|---|
*name*, *first_name*, *last_name*, *full_name* | Name | No |
*email*, *e_mail* | Email address | No |
*phone*, *mobile*, *telephone* | Phone number | No |
*address*, *street*, *city*, *postcode*, *zip* | Postal address | No |
*dob*, *date_of_birth*, *birth_date* | Date of birth | No |
*ssn*, *social_security*, *tax_id*, *national_id* | National identifier | No |
*salary*, *compensation*, *bank_account*, *iban* | Financial data | No |
*ip_address*, *ip_addr* | IP address | No |
*diagnosis*, *medical*, *health*, *condition* | Health data | Yes |
*genetic*, *dna*, *genome* | Genetic data | Yes |
*biometric*, *fingerprint*, *facial* | Biometric data | Yes |
*ethnicity*, *race*, *ethnic_origin* | Racial/ethnic origin | Yes |
*religion*, *religious*, *belief* | Religious beliefs | Yes |
*union*, *trade_union* | Trade union membership | Yes |
*political*, *party* | Political opinions | Yes |
*criminal*, *conviction*, *offence* | Criminal data (Art. 10) | Art. 10 |
Automated Field Population Logic
Art. 30(1)(a) — Controller Identity
Source: Azure AD tenant configuration + organisation management. Automation: Fully automated from AD tenant metadata.
Show full SKILL.md (469 more words)Show less
Art. 30(1)(b) — Purposes
Source: Application descriptions in AD enterprise application registry + API documentation. Automation: Partially automated. The system name and description provide a starting point, but specific purpose articulation requires human refinement to meet Art. 5(1)(b) specificity requirements.
Approach: Generate a draft purpose from the application description, flag it as "DRAFT — requires DPO review," and assign to the processing owner for refinement.
Art. 30(1)(c) — Data Subject and Data Categories
Source: Database schema analysis + API request/response schemas. Automation: Highly automated for data categories (column name matching). Data subject categories require inference from table context.
Art. 30(1)(d) — Recipients
Source: AD security groups (internal recipients) + API gateway logs (external integrations) + cloud service catalog (processors). Automation: Highly automated. AD groups reveal internal access. API gateway reveals external data flows. Cloud catalog identifies processors.
Art. 30(1)(e) — International Transfers
Source: Cloud service region configuration + API gateway destination IPs + CDN configuration. Automation: Highly automated. If a cloud service is deployed in a non-EEA region, or API calls route to non-EEA endpoints, the system flags a potential international transfer.
Art. 30(1)(f) — Retention Periods
Source: Database table statistics (oldest records), backup retention policies, lifecycle management rules. Automation: Partially automated. Can detect actual retention (how long data exists) but not intended retention (how long it should exist). Flags discrepancies between actual and policy retention.
Art. 30(1)(g) — Security Measures
Source: Cloud security configuration (encryption settings, access policies), AD conditional access policies, WAF rules, certificate configuration. Automation: Highly automated for technical measures. Organisational measures require manual documentation.
Automation Coverage Summary
| Art. 30(1) Field | Automation Level | Human Review Required |
|---|---|---|
| (a) Controller identity | High (90%) | Verify legal entity name, DPO current |
| (b) Purposes | Low (30%) | Purpose must be articulated specifically |
| (c) Data subject categories | Medium (60%) | Verify inferred categories |
| (c) Personal data categories | High (85%) | Verify pattern-matched columns |
| (d) Recipients | High (80%) | Verify external recipients, add DPA references |
| (e) International transfers | High (85%) | Verify transfer mechanisms |
| (f) Retention periods | Low (25%) | Define policy retention, not just actual |
| (g) Security measures | Medium (70%) | Add organisational measures |
Implementation Architecture
┌─────────────────────┐ ┌──────────────────┐ ┌────────────────────┐
│ Azure AD / Okta │ │ Cloud Catalog │ │ API Gateway Logs │
│ (Org structure, │ │ (AWS/Azure/GCP │ │ (Data flows, │
│ app registrations,│ │ service list, │ │ external calls, │
│ security groups) │ │ regions, tags) │ │ volumes) │
└────────┬────────────┘ └────────┬─────────┘ └─────────┬──────────┘
│ │ │
▼ ▼ ▼
┌────────────────────────────────────────────────────────────────────┐
│ RoPA Auto-Generation Engine │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ AD Parser │ │ Cloud Parser │ │ API Log Analyzer │ │
│ └──────┬───────┘ └──────┬───────┘ └──────────┬───────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Field Mapping and Population Engine │ │
│ │ • Pattern matching for personal data columns │ │
│ │ • Data flow graph construction │ │
│ │ • Transfer detection (non-EEA region identification) │ │
│ │ • Security control extraction │ │
│ └────────────────────────┬───────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────┐ │
│ │ Draft RoPA Generator │ │
│ │ • Creates draft entries with populated fields │ │
│ │ • Flags fields requiring human review │ │
│ │ • Assigns to processing owners for validation │ │
│ └────────────────────────┬───────────────────────────────────┘ │
└───────────────────────────┼───────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────┐
│ Database Schema Scanner │
│ (Connects to identified databases, extracts schemas, │
│ applies PII detection patterns) │
└────────────────────────────────────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────┐
│ RoPA Management Platform │
│ (OneTrust / TrustArc / Collibra / Custom) │
│ Draft entries queued for DPO and processing owner review │
└────────────────────────────────────────────────────────────────────┘Limitations and Risk Mitigation
False positives in PII detection: Column names like "company_address" may be flagged as personal data when they are business data. Mitigation: human review of all auto-detected PII columns.
Missing context for purposes: Automated systems cannot infer the business purpose of processing from technical metadata alone. Mitigation: purposes are always flagged as "DRAFT" and require human articulation.
Shadow IT blind spots: Systems not registered in AD or cloud catalogs will not be discovered. Mitigation: combine automated discovery with periodic manual questionnaires to capture unregistered processing.
Encrypted or tokenised data: If data is encrypted at the column level or tokenised, schema analysis may not detect personal data. Mitigation: maintain a manual register of encryption/tokenisation mappings.
Consent and lawful basis: Cannot be determined from technical metadata. Always requires legal/DPO input.
© 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/automated-ropa-generation 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
Automated Ropa Generation 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Automated Ropa Generation this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| System Designopenxlings/xlings | 615 | 1 repos | ~328 | Automated safety check: Pass | Apache-2.0 | |
| Redglebis/claude-skills | 391 | — | ~881 | Automated safety check: Pass | MIT | |
| Codebase Explorationgiancarloerra/SocratiCode | 3.3k | 1 repos | ~1.5k | Automated safety check: Pass | AGPL-3.0 | |
| SQL Optimization Patternsynulihao/AgentSkillOS | 618 | 11 repos | ~3.3k | Automated safety check: Pass | None | |
| Datamodellmnimbalyst/nimbalyst | 1.9k | — | ~713 | Automated safety check: Pass | MIT |
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Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing.
Dpia Mitigation Plan
mukul975/Privacy-Data-Protection-Skills
Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d).
Gdpr Accountability
mukul975/Privacy-Data-Protection-Skills
Guides implementation of the GDPR accountability principle under Articles 5(2) and 24, including documentation requirements for policies, DPIAs, RoPA, training records, and breach logs.
Pia Threshold Screening
mukul975/Privacy-Data-Protection-Skills
Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35.
Related
Questions about Automated Ropa Generation
What does Automated Ropa Generation do?
Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Automated Ropa Generation is an agent skill from mukul975/Privacy-Data-Protection-Skills. Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas.
When should I use Automated Ropa Generation?
Automated Ropa Generation fits situations like: tasks that involve Microservices; tasks that involve Database schema design; tasks that involve Red teaming and adversary simulation.
How do I install Automated Ropa Generation in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a claude-code`. Or copy the skill folder (skills/privacy/automated-ropa-generation in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/automated-ropa-generation in your project. Claude Code loads it when a task matches its description.
How do I install Automated Ropa Generation in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill automated-ropa-generation -a codex`. Or copy the skill folder (skills/privacy/automated-ropa-generation in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/automated-ropa-generation in your project. Codex loads it when a task matches its description.
Can I use Automated Ropa Generation 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 automated-ropa-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/automated-ropa-generation, .gemini/skills/automated-ropa-generation, .github/skills/automated-ropa-generation and .opencode/skills/automated-ropa-generation in your project.
What does Automated Ropa Generation need to run?
Going by SKILL.md and its folder, Automated Ropa Generation needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Automated Ropa Generation 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 Automated Ropa Generation 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 Automated Ropa Generation use?
Automated Ropa Generation 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 Automated Ropa Generation use?
About 3.7k tokens (SKILL.md is roughly 15k 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 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Automated Ropa Generation?
Skills that share tags, products or a category with Automated Ropa Generation: System Design (openxlings/xlings, 615 stars), Red (glebis/claude-skills, 391 stars), Codebase Exploration (giancarloerra/SocratiCode, 3.3k stars) and SQL Optimization Patterns (ynulihao/AgentSkillOS, 618 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Automated Ropa Generation?
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.