Compliance Testing
petrkindlmann/qa-skills
Test for regulatory compliance: GDPR/CMP consent verification, Google Consent Mode v2, Global Privacy Control (GPC), CCPA/US state opt-out, EU AI Act Article 50 transparency, Better Ads Standards…
Implements AI transparency requirements under EU AI Act Arts.
Install AI Transparency Reqs
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-transparency-reqs -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-transparency-reqs --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-transparency-reqs .claude/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .claude/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqsType 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-transparency-reqs -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-transparency-reqs --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-transparency-reqs .agents/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .agents/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqs -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-transparency-reqs --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-transparency-reqs .cursor/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .cursor/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqs--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-transparency-reqs -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-transparency-reqs --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-transparency-reqs .gemini/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .gemini/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqsInstalls 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-transparency-reqs -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-transparency-reqs .github/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .github/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqs -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-transparency-reqs --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-transparency-reqs .opencode/skills/ai-transparency-reqs && 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-transparency-reqs" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-transparency-reqs into .opencode/skills/ai-transparency-reqs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-transparency-reqs", 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-transparency-reqs- GitHub stars
- 301
- Token cost
- ~3k tokens
- SKILL.md length
- 1,343 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Implements AI transparency requirements under EU AI Act Arts.
- Works in 6 steps: System-level explanation: General… → Purpose and context: Why the AI system… → Key variables: The main data points or… → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, GDPR Transparency for AI Systems, EU AI Act Transparency… and AI Transparency Documentation…, plus 3 more sections
- Runs Python scripts from its folder
What it does
AI Transparency Reqs is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
Its SKILL.md is about 3k 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 and AI governance. 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
- Tasks that involve AI governance
Example prompts
- “Use the ai-transparency-reqs skill to implement AI transparency requirements under EU AI Act Arts”
- “/ai-transparency-reqs”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- System-level explanation: General description of how the AI system works — what factors are considered, what methodology is used, how the…
- Purpose and context: Why the AI system is used and what role its output plays in decisions
- Key variables: The main data points or features that influence the AI output, without requiring disclosure of proprietary algorithms
- Decision criteria: How the model output translates into a decision (e.g., score thresholds, classification categories)
- Significance: What the decision means for the data subject in practical terms
- Consequences: The potential effects (both intended and foreseeable) of the AI-driven decision
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 Transparency Reqs loads about 3k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,343 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). 1,343 words, ~2,959 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-transparency-reqs/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-transparency-reqs
- description
- Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- ai-transparency, eu-ai-act, gdpr-notification, explainability, disclosure, automated-logic
AI Transparency Requirements
Overview
AI transparency operates at the intersection of two regulatory frameworks: the GDPR's data subject information rights (Arts. 13-14) and the EU AI Act's transparency obligations (Arts. 13-14, 50). Together they require controllers and deployers to provide meaningful, accessible information about AI system capabilities, limitations, decision logic, and personal data processing. This skill implements the combined transparency framework, addressing both the technical explainability challenge of complex ML models and the legal obligation to communicate AI processing in plain language to affected individuals.
GDPR Transparency for AI Systems
Art. 13-14 Information Requirements Applied to AI
When personal data is processed by AI systems, data subjects must receive:
| Information Element | GDPR Article | AI-Specific Application |
|---|---|---|
| Purposes of processing | Art. 13(1)(c) / 14(1)(c) | Specific AI use case, not generic "service improvement" |
| Lawful basis | Art. 13(1)(c) / 14(1)(c) | The basis for AI training and for AI inference separately |
| Legitimate interest | Art. 13(1)(d) / 14(2)(b) | The specific interest served by AI processing |
| Recipients | Art. 13(1)(e) / 14(1)(e) | AI infrastructure providers, model hosting services |
| International transfers | Art. 13(1)(f) / 14(1)(f) | Where AI processing occurs (training and inference locations) |
| Retention period | Art. 13(2)(a) / 14(2)(a) | Training data retention, inference log retention, model lifecycle |
| Data subject rights | Art. 13(2)(b) / 14(2)(c) | Including AI-specific rights: explanation, contestation, human review |
| Automated decision-making | Art. 13(2)(f) / 14(2)(g) | Meaningful information about logic, significance, and envisaged consequences |
| Source of data | Art. 14(2)(f) | Training data sources (categories, not necessarily individual sources) |
Art. 13(2)(f) / 14(2)(g) — Meaningful Information About Automated Logic
This is the most challenging transparency requirement for AI systems. The EDPB and Article 29 Working Party have clarified:
What "meaningful information about the logic involved" requires:
- System-level explanation: General description of how the AI system works — what factors are considered, what methodology is used, how the model was trained
- Purpose and context: Why the AI system is used and what role its output plays in decisions
- Key variables: The main data points or features that influence the AI output, without requiring disclosure of proprietary algorithms
- Decision criteria: How the model output translates into a decision (e.g., score thresholds, classification categories)
- Significance: What the decision means for the data subject in practical terms
- Consequences: The potential effects (both intended and foreseeable) of the AI-driven decision
What it does not require:
- Full disclosure of source code or model weights
- Mathematical description of the algorithm
- Proprietary trade secrets (but this does not exempt from meaningful explanation)
- Explanation of individual model predictions in real-time (though this may be required under Art. 22)
Layered Approach to AI Transparency
The EDPB recommends a layered transparency approach:
| Layer | Content | Delivery |
|---|---|---|
| Layer 1: Initial notice | AI is used in processing; general purpose; link to full information | At point of interaction (banner, tooltip, notification) |
| Layer 2: Summary | AI system description, key data used, decision logic summary, rights available | Privacy notice section, AI information page |
| Layer 3: Detailed information | Full technical description, training data categories, fairness measures, accuracy metrics, limitations | Supplementary documentation, upon request |
| Layer 4: Individual explanation | Specific factors influencing a particular decision, appeal mechanism | Upon request or automatically for significant decisions |
EU AI Act Transparency Obligations
Art. 13 — Transparency and Provision of Information to Deployers (High-Risk)
High-risk AI systems (Annex III) must be designed and developed to ensure:
| Requirement | Description |
|---|---|
| Interpretability | System design enables deployers to interpret outputs and use them appropriately |
| Instructions for use | Detailed documentation of capabilities, limitations, intended purpose, foreseeable misuse |
| Performance metrics | Accuracy levels, robustness metrics, known limitations for specific groups |
| Human oversight info | Description of human oversight measures and how to implement them |
| Input data specs | Description of input data the system was designed to process |
| Training data description | Relevant information about training data including provenance and preprocessing |
Art. 14 — Human Oversight (High-Risk)
High-risk AI systems must be designed to enable effective human oversight:
- Clear indication of AI system outputs and confidence levels
- Ability to correctly interpret AI outputs in context
- Ability to override or reverse AI decisions
- Ability to intervene or stop the system ("stop button")
- Awareness of automation bias risk
Art. 50 — Transparency for Specific AI Systems
| AI System Type | Transparency Obligation |
|---|---|
| AI interacting with persons | Inform that they are interacting with an AI system (unless obvious from context) |
| Emotion recognition / biometric categorisation | Inform about the system's operation and process personal data in compliance with GDPR |
| AI-generated or manipulated content (deepfakes) | Label content as AI-generated in a machine-readable format |
| AI-generated text on matters of public interest | Disclose that the text has been artificially generated or manipulated |
Art. 50(1) — AI Interaction Notification
Controllers must inform natural persons that they are interacting with an AI system. This applies to:
- Chatbots and virtual assistants
- AI-powered customer service systems
- Automated email or message generation
- AI-driven recommendation systems with direct user interface
- Voice-based AI assistants
Exceptions: where it is obvious from the circumstances and context that the person is interacting with AI (e.g., a robot in a factory setting).
Show full SKILL.md (534 more words)Show less
AI Transparency Documentation Framework
Model Card Requirements
For each deployed AI model, maintain a model card containing:
| Section | Content |
|---|---|
| Model overview | Name, version, type, developer, deployment date |
| Intended use | Specific purpose, target users, deployment context |
| Out-of-scope use | Uses the model is not designed for; foreseeable misuse |
| Training data summary | Data sources (categories), volume, temporal range, geographic scope, known biases |
| Performance metrics | Accuracy, precision, recall, F1 by relevant subgroup; fairness metrics |
| Limitations | Known failure modes, demographic performance disparities, edge cases |
| Privacy properties | Differential privacy applied (epsilon), membership inference test results, training data extraction risk |
| Human oversight | Level of oversight required, reviewer qualifications, override procedures |
| Update history | Retraining dates, data updates, performance changes |
AI System Transparency Register
Organisations operating multiple AI systems should maintain a central register:
| Field | Description |
|---|---|
| System ID | Unique identifier |
| System name | Human-readable name |
| AI Act classification | Unacceptable / High / Limited / Minimal |
| Purpose | Specific processing purpose |
| Data subjects affected | Categories and estimated numbers |
| Personal data processed | At training and inference |
| Decision authority | AI decision-support vs. automated decision |
| Transparency measures | Notification, explanation, documentation |
| Deployer | Internal / External deployment |
| Registration date | EU AI Act database registration (if high-risk) |
Explainability Techniques for Compliance
Global Explainability (System-Level)
Techniques for providing Art. 13(2)(f) "meaningful information about the logic":
| Technique | Best For | Limitation |
|---|---|---|
| Feature importance (SHAP, LIME) | Identifying key variables | May oversimplify complex interactions |
| Decision rules extraction | Converting model logic to human-readable rules | Loss of accuracy for complex models |
| Partial dependence plots | Showing how features affect predictions | Assumes feature independence |
| Counterfactual explanations | Showing what change would lead to different outcome | Computationally expensive for many features |
| Attention visualisation | Transformer models — showing what the model focuses on | Attention does not always equal importance |
Local Explainability (Individual Decision)
For Art. 22 right to explanation of individual decisions:
| Technique | Description | Use Case |
|---|---|---|
| LIME | Local Interpretable Model-agnostic Explanations | Any model — approximate local behaviour with interpretable model |
| SHAP values | Shapley Additive Explanations for individual predictions | Feature contribution to specific prediction |
| Counterfactual | "You were denied because X; if X were Y, outcome would be different" | Credit, hiring, insurance decisions |
| Anchors | Sufficient conditions for a prediction | Rule-based explanation of individual case |
| Concept-based | High-level concepts that influenced the decision | When features are not directly interpretable |
Enforcement Precedents
- Garante v. OpenAI (2023): Required transparency about AI training data processing, model capabilities, and limitations — privacy notice deemed insufficient for AI system transparency.
- CNIL v. Clearview AI (SAN-2022-019, 2022): EUR 20M fine — complete absence of transparency about facial recognition AI processing; data subjects had no notice their images were scraped and processed.
- Austrian DPA v. CRIF (DSB-D213.636, 2023): Credit scoring AI — insufficient explanation of automated decision-making logic per Art. 13(2)(f); data subject received only a score without meaningful information about factors.
- Dutch DPA v. Tax Authority (SyRI, 2020): Court found algorithmic fraud detection lacked transparency — citizens could not understand how the system assessed them, violating right to private life.
- AEPD v. CaixaBank (PS/00421/2020, 2021): EUR 6M fine — automated credit decision-making without adequate transparency about the logic involved and significance for data subjects.
Integration Points
- ai-automated-decisions: Art. 22 explanation requirements integrate with transparency obligations
- ai-dpia: Transparency assessment is part of DPIA necessity and proportionality analysis
- ai-act-high-risk-docs: Art. 13 AI Act documentation requirements overlap with transparency framework
- ai-deployment-checklist: Pre-deployment transparency validation is a checklist item
© 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-transparency-reqs 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 Transparency Reqs 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 |
|---|---|---|---|---|---|---|
| AI Transparency Reqs this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Compliance Testingpetrkindlmann/qa-skills | 170 | — | ~4.6k | Automated safety check: Pass | MIT | |
| Compliance Osalirezarezvani/claude-skills | 28k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Ra Qm Skillsalirezarezvani/claude-skills | 28k | — | ~833 | Automated safety check: Pass | MIT | |
| Cross Regulatory Impact Analyzer Patrick Munrolawve-ai/awesome-legal-skills | 847 | — | ~3.1k | Automated safety check: Pass | AGPL-3.0 | |
| Regulatory Deal Card Generator Patrick Munrolawve-ai/awesome-legal-skills | 847 | — | ~2.1k | Automated safety check: Pass | AGPL-3.0 |
Similar skills
Compliance Os
alirezarezvani/claude-skills
Compliance OS — meta-orchestrator that lets compliance teams CONFIGURE which frameworks apply, COMPUTE cross-framework control overlap, SIMULATE internal audits, and CONSOLIDATE evidence across…
Ra Qm Skills
alirezarezvani/claude-skills
Router/index for the 15 regulatory & quality-management skills bundled in this plugin (ISO 13485 QMS, EU MDR 2017/745, FDA submissions under QMSR, ISO 14971 risk, CAPA, document control, ISO…
Cross Regulatory Impact Analyzer Patrick Munro
lawve-ai/awesome-legal-skills
Analyzes how multiple regulations interact for a specific product, service, or business model.
Regulatory Deal Card Generator Patrick Munro
lawve-ai/awesome-legal-skills
Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable…
Repo Prep
glebis/claude-skills
Interactively prepare a code repository for publication — LICENSE, NOTICE, AUTHORSHIP, README sections, package metadata, .gitignore, community docs (CONTRIBUTING/CODEOFCONDUCT/SECURITY/CHANGELOG)…
More from mukul975/Privacy-Data-Protection-Skills
All 280 skills in this repoAge Gating Services
mukul975/Privacy-Data-Protection-Skills
Implements age-gating mechanisms for online services to restrict access based on user age.
AI Data Retention
mukul975/Privacy-Data-Protection-Skills
Manages AI model retention and machine unlearning requirements.
AI Dpia
mukul975/Privacy-Data-Protection-Skills
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
Categories
Questions about AI Transparency Reqs
What does AI Transparency Reqs do?
Implements AI transparency requirements under EU AI Act Arts. AI Transparency Reqs is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements AI transparency requirements under EU AI Act Arts.
When should I use AI Transparency Reqs?
AI Transparency Reqs fits situations like: tasks that involve Privacy and GDPR; tasks that involve AI governance.
How do I install AI Transparency Reqs in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-transparency-reqs -a claude-code`. Or copy the skill folder (skills/privacy/ai-transparency-reqs in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-transparency-reqs in your project. Claude Code loads it when a task matches its description.
How do I install AI Transparency Reqs in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-transparency-reqs -a codex`. Or copy the skill folder (skills/privacy/ai-transparency-reqs in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-transparency-reqs in your project. Codex loads it when a task matches its description.
Can I use AI Transparency Reqs 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-transparency-reqs -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-transparency-reqs, .gemini/skills/ai-transparency-reqs, .github/skills/ai-transparency-reqs and .opencode/skills/ai-transparency-reqs in your project.
What does AI Transparency Reqs need to run?
Going by SKILL.md and its folder, AI Transparency Reqs needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Transparency Reqs 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 Transparency Reqs 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 Transparency Reqs use?
AI Transparency Reqs 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 Transparency Reqs use?
About 3k tokens (SKILL.md is roughly 12k 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 4.7k tokens, read only when the agent opens those files.
What are the alternatives to AI Transparency Reqs?
Skills that share tags, products or a category with AI Transparency Reqs: Compliance Testing (petrkindlmann/qa-skills, 170 stars), Compliance Os (alirezarezvani/claude-skills, 28k stars), Ra Qm Skills (alirezarezvani/claude-skills, 28k stars) and Cross Regulatory Impact Analyzer Patrick Munro (lawve-ai/awesome-legal-skills, 847 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Transparency Reqs?
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.