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Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing.
Install AI Dpia
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-dpia -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-dpia --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/ai-dpia .claude/skills/ai-dpia && 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 "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .claude/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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/ai-dpiaType 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 ai-dpia -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-dpia --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/ai-dpia .agents/skills/ai-dpia && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .agents/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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 ai-dpia -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-dpia --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/ai-dpia .cursor/skills/ai-dpia && 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 "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .cursor/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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/ai-dpia--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 ai-dpia -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-dpia --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/ai-dpia .gemini/skills/ai-dpia && 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 "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .gemini/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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 ai-dpiaInstalls 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 ai-dpia -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/ai-dpia .github/skills/ai-dpia && 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 "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .github/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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 ai-dpia -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 ai-dpia --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/ai-dpia .opencode/skills/ai-dpia && 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 "ai-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-dpia into .opencode/skills/ai-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-dpia", 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
ai-dpia- GitHub stars
- 301
- Token cost
- ~3.4k tokens
- SKILL.md length
- 1,575 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing.
- Works in 5 steps: AI System Description (Art. 35(7)(a)… → AI-Specific Necessity and Proportionality → AI-Specific Risk Assessment → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, AI-Specific DPIA Triggers, AI DPIA Methodology — EDPB… and Prior Consultation Triggers…, plus 2 more sections
- Runs Python scripts from its folder
What it does
AI Dpia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.
Its SKILL.md is about 3.4k 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
- “Use the ai-dpia skill to conduct Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing”
- “/ai-dpia”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
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
AI Dpia loads about 3.4k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,575 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). 1,575 words, ~3,420 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-dpia/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-dpia
- description
- Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- ai-dpia, machine-learning, edpb-guidelines, model-risk, training-data, impact-assessment
Data Protection Impact Assessment for AI/ML Systems
Overview
AI and ML systems present unique privacy challenges that traditional DPIA methodologies fail to adequately address. The EDPB Guidelines 04/2025 on processing personal data through AI systems establish a specialized framework that supplements the general DPIA requirements of GDPR Article 35 and WP248rev.01. AI-specific DPIAs must evaluate the entire ML pipeline — from training data collection through model deployment and inference — assessing risks that emerge from statistical learning, emergent model behaviours, and the opacity of algorithmic decision-making. This skill implements the EDPB's AI-specific DPIA methodology integrated with the EU AI Act risk classification framework.
AI-Specific DPIA Triggers
Mandatory DPIA Triggers for AI Systems
All AI processing that meets any of the following criteria requires a DPIA before deployment:
| Trigger | Legal Basis | Description |
|---|---|---|
| AI-based profiling with legal effects | Art. 35(3)(a) GDPR | ML models that produce decisions with legal or similarly significant effects on natural persons (credit scoring, hiring, insurance pricing) |
| Training on special category data | Art. 35(3)(b) GDPR | Models trained on health, biometric, genetic, racial, political, religious, sexual orientation, or trade union data at scale |
| AI-powered surveillance | Art. 35(3)(c) GDPR | Computer vision, facial recognition, behavioural analytics, or anomaly detection in public spaces |
| High-risk AI systems | Art. 6 EU AI Act | Systems listed in Annex III of the AI Act (biometric identification, critical infrastructure, employment, law enforcement, migration, justice) |
| Foundation models processing personal data | EDPB Guidelines 04/2025 | LLMs and foundation models trained on datasets containing personal data, regardless of downstream use |
| Automated inference of sensitive attributes | EDPB Guidelines 04/2025 | Models that infer Art. 9 special category data from non-sensitive inputs (inferring health status from purchasing patterns) |
EDPB WP248 Criteria Applied to AI
AI systems frequently trigger multiple WP248 criteria simultaneously:
- Evaluation/scoring: Inherent to classification and regression models
- Automated decision-making: Core function of deployed AI systems
- Innovative technology: Novel model architectures, training techniques
- Large-scale processing: Training datasets containing millions of records
- Matching/combining datasets: Multi-source training data aggregation
- Vulnerable data subjects: When AI is applied to children, employees, patients
When an AI system meets two or more criteria, a DPIA is presumptively required.
AI DPIA Methodology — EDPB Framework
Phase 1: AI System Description (Art. 35(7)(a) Extended)
The systematic description must cover the complete AI lifecycle:
1.1 Training Phase Documentation
- Training data sources: Origin, collection method, consent status, lawful basis for each dataset
- Data categories: All personal data categories present in training data, including data that may be inadvertently included (background individuals in images, metadata in text corpora)
- Data volume: Number of records, data subjects affected, geographic scope
- Data preprocessing: Cleaning, augmentation, labelling processes and any human review
- Feature engineering: Which personal data attributes are used as features, how derived features are computed
- Training infrastructure: Where training occurs (cloud provider, jurisdiction), data residency during training
1.2 Model Architecture Documentation
- Model type: Neural network architecture (transformer, CNN, RNN), ensemble methods, decision trees
- Model parameters: Number of parameters, model size, complexity indicators
- Explainability characteristics: Inherent interpretability level (white-box, grey-box, black-box)
- Memorization risk: Assessed propensity for the model to memorize training data (higher for large models with small datasets)
1.3 Deployment Phase Documentation
- Inference inputs: What personal data is processed at inference time
- Output types: Classifications, scores, recommendations, generated content
- Decision pipeline: How model outputs feed into decisions affecting data subjects
- Human oversight: Level and effectiveness of human review in the decision chain
- Monitoring: Drift detection, performance monitoring, feedback loops
Phase 2: AI-Specific Necessity and Proportionality
2.1 Purpose Limitation for AI
- Is the AI system necessary for the stated purpose, or could simpler processing achieve it?
- Has the controller evaluated non-AI alternatives and documented why AI is required?
- Are the training data processing purposes compatible with the original collection purposes (Art. 6(4) assessment)?
- For repurposed data: has a compatibility assessment been conducted per EDPB Guidelines 04/2025?
2.2 Data Minimisation for AI
- Has the minimum dataset required for acceptable model performance been determined through ablation studies?
- Can synthetic data, federated learning, or differential privacy reduce the personal data requirement?
- Are there personal data elements in the training data that do not contribute to model performance?
- Has the controller assessed whether anonymised or pseudonymised data could achieve adequate performance?
2.3 Training Data Lawfulness Assessment
For each training dataset, document:
| Assessment Element | Requirement |
|---|---|
| Original collection purpose | Was personal data collected for a purpose compatible with AI training? |
| Lawful basis | Art. 6(1) basis for the training processing — legitimate interest requires balancing test |
| Consent validity | If consent is the basis, was AI training specified as a purpose? Was consent freely given? |
| Special category conditions | If Art. 9 data is present, which Art. 9(2) exception applies? |
| Web-scraped data | EDPB position: web scraping for AI training generally cannot rely on legitimate interest without additional safeguards |
| Third-party datasets | Has the controller verified the upstream lawful basis chain? |
Phase 3: AI-Specific Risk Assessment
3.1 Privacy Risk Categories for AI
| Risk Category | Description | Likelihood Factors |
|---|---|---|
| Training data extraction | Adversary extracts verbatim training data from the model | Model size, training data repetition, overfitting degree |
| Membership inference | Adversary determines if specific data was in the training set | Model confidence distribution, overfitting, shadow model availability |
| Model inversion | Adversary reconstructs input features from model outputs | Output granularity, model type, auxiliary information available |
| Attribute inference | Model reveals sensitive attributes not provided as input | Correlations in training data, feature interactions |
| Emergent bias amplification | Model amplifies biases present in training data, producing discriminatory outcomes | Training data representativeness, debiasing measures applied |
| Concept drift discrimination | Model performance degrades unequally across demographic groups over time | Monitoring coverage, retraining frequency |
| Re-identification through AI output | Model outputs enable linking back to specific data subjects | Output specificity, population uniqueness, auxiliary data |
| Automated decision errors | Incorrect AI decisions causing material harm to data subjects | Model accuracy, error distribution across groups |
Show full SKILL.md (630 more words)Show less
3.2 AI Risk Scoring Matrix
Combine likelihood and severity using the EDPB-recommended matrix:
Negligible Limited Significant Maximum
Almost Certain Medium High Very High Very High
Likely Medium High High Very High
Possible Low Medium High High
Remote Low Low Medium High3.3 AI Act Risk Level Integration
Cross-reference GDPR risk assessment with AI Act classification:
- Unacceptable risk (Art. 5 AI Act): Processing must not proceed — social scoring, real-time biometric identification in public spaces (with limited exceptions)
- High risk (Art. 6 + Annex III): Enhanced DPIA obligations, conformity assessment required
- Limited risk (Art. 50): Transparency obligations — inform users they are interacting with AI
- Minimal risk: Standard DPIA process applies
Phase 4: AI-Specific Mitigation Measures
Technical Measures
| Measure | Risk Addressed | Implementation |
|---|---|---|
| Differential privacy | Training data extraction, membership inference | Apply DP-SGD during training with calibrated epsilon (ε ≤ 8 for moderate protection, ε ≤ 1 for strong) |
| Federated learning | Data centralisation risk | Distribute training across data holders without centralising personal data |
| Model output perturbation | Model inversion, attribute inference | Add calibrated noise to model outputs, round confidence scores |
| Training data deduplication | Memorization risk | Remove duplicate and near-duplicate records before training |
| Membership inference testing | Membership inference | Run MI attacks against the model pre-deployment; retrain if leakage exceeds threshold |
| Fairness constraints | Bias amplification | Apply demographic parity, equalised odds, or calibration constraints during training |
| Input/output filtering | PII leakage in generative models | Deploy PII detection on model inputs and outputs with automated redaction |
| Model pruning and distillation | Memorization, extraction | Compress the model to reduce capacity for memorizing individual records |
Organisational Measures
- Establish an AI Ethics Review Board with privacy representation
- Implement model cards documenting privacy properties for each deployed model
- Conduct regular model audits (minimum annually) testing for privacy leakage
- Maintain training data provenance documentation and deletion capability
- Define retraining triggers and ensure DPIA review accompanies each retraining cycle
- Implement incident response procedures specific to AI privacy incidents
Phase 5: Human Oversight Assessment
Per AI Act Art. 14 and GDPR Art. 22, assess the human oversight mechanism:
| Oversight Element | Assessment Question |
|---|---|
| Meaningful review | Can the human reviewer effectively evaluate the AI recommendation and override it? |
| Time and resources | Is sufficient time allocated for meaningful review, or is the human a rubber stamp? |
| Competence | Does the reviewer have the expertise to identify AI errors? |
| Authority | Does the reviewer have the authority and means to override the AI? |
| Feedback mechanism | Are overrides recorded and fed back into model improvement? |
| Automation bias | Are measures in place to mitigate the tendency to defer to the AI? |
Prior Consultation Triggers for AI
Art. 36 prior consultation with the supervisory authority is required when:
- The AI system produces high residual risk after all mitigation measures
- The AI system processes special category data at scale with novel techniques
- The supervisory authority's Art. 35(4) list specifically includes the AI use case
- The AI system is deployed for real-time biometric identification under AI Act Art. 5 exceptions
Enforcement Precedents
- Clearview AI (Multiple DPAs, 2021-2024): Fines totalling over EUR 90 million across Italy (EUR 20M), France (EUR 20M), UK (GBP 7.5M), Greece (EUR 20M) for facial recognition AI deployed without DPIA, lawful basis, or transparency
- CNIL v. Clearview AI (SAN-2022-019): Specific finding that no DPIA was conducted for biometric AI processing
- Italian DPA v. Replika (2023): Ordered cessation of AI chatbot processing due to inadequate age verification and failure to conduct DPIA for AI processing affecting minors
- Spanish DPA v. CaixaBank (PS/00421/2020): EUR 6 million fine for automated credit scoring without adequate DPIA addressing algorithmic decision-making risks
- Dutch DPA v. Tax Authority (2020): Finding that algorithmic fraud detection system (SyRI) lacked proportionality and adequate DPIA for AI-driven profiling
Integration Points
- ai-training-lawfulness: Detailed lawful basis analysis for training data feeds into Phase 2
- ai-automated-decisions: Art. 22 assessment integrates with Phase 5 human oversight
- ai-model-privacy-audit: Technical privacy testing results feed into Phase 3 risk assessment
- ai-act-high-risk-docs: AI Act conformity assessment aligns with Phase 3.3 risk classification
- ai-bias-special-category: Bias assessment results feed into risk scoring for discrimination harms
© 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/ai-dpia 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
AI Dpia 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 |
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| AI Dpia this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| C15tc15t/c15t | 1.9k | 1 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Korean Privacy Termskimlawtech/korean-privacy-terms | 587 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~2.3k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about AI Dpia
What does AI Dpia do?
Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. AI Dpia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing.
When should I use AI Dpia?
AI Dpia fits situations like: tasks that involve Privacy and GDPR.
How do I install AI Dpia in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-dpia -a claude-code`. Or copy the skill folder (skills/privacy/ai-dpia in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-dpia in your project. Claude Code loads it when a task matches its description.
How do I install AI Dpia in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-dpia -a codex`. Or copy the skill folder (skills/privacy/ai-dpia in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-dpia in your project. Codex loads it when a task matches its description.
Can I use AI Dpia 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 ai-dpia -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-dpia, .gemini/skills/ai-dpia, .github/skills/ai-dpia and .opencode/skills/ai-dpia in your project.
What does AI Dpia need to run?
Going by SKILL.md and its folder, AI Dpia needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Dpia 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 AI Dpia 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 AI Dpia use?
AI Dpia 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 AI Dpia use?
About 3.4k tokens (SKILL.md is roughly 14k 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 5.5k tokens, read only when the agent opens those files.
What are the alternatives to AI Dpia?
Skills that share tags, products or a category with AI Dpia: 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 AI Dpia?
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.