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…
Classifies sensitive data in AI/ML training datasets including bias detection for Art.
Install AI Training Data Class
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-training-data-class -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-training-data-class --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-training-data-class .claude/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .claude/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-classType 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-training-data-class -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-training-data-class --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-training-data-class .agents/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .agents/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-class -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-training-data-class --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-training-data-class .cursor/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .cursor/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-class--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-training-data-class -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-training-data-class --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-training-data-class .gemini/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .gemini/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-classInstalls 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-training-data-class -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-training-data-class .github/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .github/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-class -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-training-data-class --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-training-data-class .opencode/skills/ai-training-data-class && 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-training-data-class" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-training-data-class into .opencode/skills/ai-training-data-class/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-training-data-class", 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-training-data-class- GitHub stars
- 295
- Token cost
- ~2.9k tokens
- SKILL.md length
- 1,302 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 278
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Classifies sensitive data in AI/ML training datasets including bias detection for Art.
- Works in 4 steps: Demographic Representation Analysis → Proxy Variable Identification → Disparate Impact Analysis → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, GDPR and AI Act Intersection, Training Data Classification… and Data Card Documentation, plus 3 more sections
- Runs Python scripts from its folder
What it does
AI Training Data Class is an agent skill from mukul975/Privacy-Data-Protection-Skills. Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
Its SKILL.md is about 2.9k 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, Fine-tuning 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 Fine-tuning
- Tasks that involve AI governance
Example prompts
- “Use the ai-training-data-class skill to classify sensitive data in AI/ML training datasets including bias detection for Art”
- “/ai-training-data-class”
Requirements
- Python 3
Workflow steps
4 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 Training Data Class loads about 2.9k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 1,302 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). 1,302 words, ~2,856 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-training-data-class/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-training-data-class
- description
- Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- data-classification
- metadata.tags
- ai-training-data, ml-dataset, bias-detection, data-card, model-training, gdpr-ai
Sensitive Data Classification for AI/ML Training Datasets
Overview
AI and machine learning models trained on personal data raise distinct classification challenges. Training data may contain direct personal data, inferred special categories, proxy variables for protected characteristics, and data whose consent scope does not extend to model training. The EU AI Act (Regulation (EU) 2024/1689) imposes additional requirements for high-risk AI systems, including data governance obligations under Art. 10 that intersect with GDPR classification requirements. This skill provides a framework for classifying training data, detecting bias-relevant features, documenting data provenance, and verifying consent coverage.
GDPR and AI Act Intersection
GDPR Requirements for Training Data
| GDPR Article | Application to AI Training |
|---|---|
| Art. 5(1)(b) — Purpose limitation | Training a model is a distinct processing purpose; if data was collected for customer service, using it for ML training requires a compatible purpose assessment or new lawful basis |
| Art. 5(1)(c) — Data minimisation | Training datasets must not include more personal data than necessary for the model objective |
| Art. 6 — Lawful basis | Model training requires its own lawful basis; legitimate interests (Art. 6(1)(f)) is most common, but requires LIA documentation |
| Art. 9 — Special categories | If training data contains or enables inference of special category data, Art. 9(2) condition required |
| Art. 22 — Automated decision-making | If the trained model makes decisions with legal or significant effects, additional safeguards apply |
| Art. 25 — Data protection by design | Classification of training data is a by-design measure enabling appropriate technical protections |
| Art. 35 — DPIA | High-risk AI processing (profiling, automated decision-making) requires DPIA |
EU AI Act Art. 10 — Data Governance for High-Risk AI
The AI Act Art. 10 requires that training, validation, and testing datasets for high-risk AI systems:
- Are subject to appropriate data governance and management practices (Art. 10(2))
- Are relevant, sufficiently representative, and to the extent possible free of errors and complete (Art. 10(3))
- Take into account the specific geographical, contextual, behavioural, or functional setting within which the AI system is intended to be used (Art. 10(4))
- Where special category data processing is strictly necessary for bias detection and correction, this is permitted under Art. 10(5), subject to appropriate safeguards including pseudonymisation and GDPR compliance
Training Data Classification Framework
Classification Tier 1: Personal Data Content
| Classification | Description | Example |
|---|---|---|
| TRAINING_PII_DIRECT | Dataset contains direct identifiers | Customer names, email addresses in NLP training corpus |
| TRAINING_PII_INDIRECT | Dataset contains indirect identifiers | Customer IDs, transaction patterns enabling singling out |
| TRAINING_SPECIAL_CAT | Dataset contains Art. 9 special category data | Health records for medical diagnosis model |
| TRAINING_CRIMINAL | Dataset contains Art. 10 criminal data | Fraud transaction labels derived from criminal investigations |
| TRAINING_PSEUDONYMISED | Personal data replaced with tokens but re-identification key exists | Pseudonymised customer data with mapping held by data team |
| TRAINING_ANONYMISED | Data verified as anonymised per WP29 criteria | Aggregated population statistics with k ≥ 10 |
| TRAINING_SYNTHETIC | Artificially generated data with no real personal data | GAN-generated synthetic transaction data |
| TRAINING_NON_PERSONAL | No personal data content | Market price data, weather data, product specifications |
Classification Tier 2: Bias and Proxy Detection
Even when a dataset does not directly contain Art. 9 special category data, it may contain proxy variables that correlate with protected characteristics:
| Proxy Variable | Correlated Protected Characteristic | Detection Method |
|---|---|---|
| Postcode/ZIP code | Racial/ethnic origin, socioeconomic status | Geographic demographic analysis |
| First name | Gender, ethnic origin, age cohort | Name demographics database lookup |
| Language preference | Ethnic origin, nationality | Statistical correlation analysis |
| Shopping patterns | Religious belief (halal/kosher purchases), health status | Purchase category analysis |
| Web browsing history | Political opinions, sexual orientation, health status | Topic modelling on browsing categories |
| Employment gap patterns | Gender (maternity), disability, health | Statistical pattern analysis |
| Credit score | Racial/ethnic origin (documented correlation in US/UK studies) | Disparate impact analysis |
Classification Tier 3: Consent and Purpose Scope
| Classification | Description | Compliance Requirement |
|---|---|---|
| CONSENT_COVERS_TRAINING | Original consent explicitly covers AI/ML training | Document consent text and verify specificity |
| CONSENT_DOES_NOT_COVER | Original consent did not anticipate ML training | New consent required or alternative lawful basis needed |
| LEGITIMATE_INTEREST | ML training relies on legitimate interests (Art. 6(1)(f)) | Documented LIA required |
| CONTRACT_PERFORMANCE | ML training is necessary for contract performance | Narrow scope — must be genuinely necessary |
| PUBLIC_DATA | Data sourced from publicly available sources | Still requires lawful basis; public availability is not a lawful basis |
| RESEARCH_EXEMPTION | Processing under Art. 89(1) research exemption | Appropriate safeguards including pseudonymisation required |
Data Card Documentation
A data card is a structured document accompanying each training dataset, providing transparency about its contents, provenance, and limitations. Modelled on the "Datasheets for Datasets" framework (Gebru et al., 2021) and adapted for GDPR compliance.
Show full SKILL.md (576 more words)Show less
Required Data Card Fields for Vanguard Financial Services
| Section | Fields |
|---|---|
| 1. Dataset Identity | Name, version, creation date, owner, purpose |
| 2. Personal Data Classification | Tier 1 classification, data elements present, classification labels |
| 3. Data Subjects | Categories of data subjects, volume, geographic scope |
| 4. Provenance | Original collection purpose, source systems, processing chain from collection to training set |
| 5. Consent/Lawful Basis | Tier 3 classification, consent text reference or LIA reference, purpose compatibility assessment |
| 6. Special Category Assessment | Whether Art. 9 data is present (direct or inferred), Art. 9(2) condition if applicable |
| 7. Bias Assessment | Proxy variables identified, disparate impact analysis results, demographic representation statistics |
| 8. De-identification | Technique applied (pseudonymisation, anonymisation, synthetic generation), assessment reference |
| 9. Retention | Training data retention period, model retention period, deletion schedule |
| 10. Access Controls | Who can access the training data, who can access the model, audit logging |
| 11. DPIA Reference | DPIA document reference if applicable |
| 12. Limitations | Known biases, geographic limitations, temporal limitations, data quality issues |
Bias Detection Methodology
Step 1: Demographic Representation Analysis
For each training dataset, calculate representation statistics:
- What percentage of records come from each demographic group (to the extent known)?
- Does the representation match the target population?
- Are any groups under-represented by more than 20% relative to population?
Step 2: Proxy Variable Identification
Scan all features for proxy correlation with Art. 9 protected characteristics:
- Calculate correlation coefficient between each feature and known protected characteristics (where available)
- Flag features with |correlation| > 0.3 as potential proxies
- Document all proxy variables in the data card
Step 3: Disparate Impact Analysis
For classification or scoring models:
- Calculate model performance metrics by demographic group
- Apply the 80% (four-fifths) rule: if the selection rate for any protected group is less than 80% of the rate for the most favoured group, disparate impact may exist
- Document disparate impact analysis results in the data card
Step 4: AI Act Art. 10(5) Assessment
If bias detection requires processing special category data:
- Document why processing is "strictly necessary" for bias detection and correction
- Implement pseudonymisation of the special category data used for bias testing
- Ensure GDPR Art. 9(2) condition is established (typically Art. 9(2)(g) substantial public interest or Art. 9(2)(j) research)
- Process in a controlled environment with access restricted to the bias assessment team
- Delete special category data after bias assessment is complete
Enforcement and Regulatory Precedents
- Italian Garante — Clearview AI (2022): EUR 20 million fine for processing biometric data scraped from public sources for AI facial recognition training without lawful basis, consent, or transparency. Established that public availability does not provide lawful basis for AI training.
- Italian Garante — ChatGPT/OpenAI (2023): Temporary ban and subsequent enforcement requiring OpenAI to establish lawful basis for training data collection, implement age verification, and provide opt-out mechanisms. Highlighted that AI training on personal data requires GDPR compliance throughout the data lifecycle.
- CNIL — Enforcement Notice on AI Training Data (2024): CNIL published guidance sheets on AI training data requiring purpose limitation assessment, proportionality analysis, and specific measures when training data contains special category data.
- ICO — Generative AI and Data Protection Consultation (2024): ICO's position that legitimate interests is the most likely lawful basis for AI training but requires documented LIA considering data subject reasonable expectations.
Integration Points
- personal-data-test: Training data must first be classified as personal or non-personal
- special-category-data: Art. 9 data in training sets requires heightened protections
- pseudo-vs-anon-data: De-identification of training data must be validated
- classification-policy: Training data classified under enterprise classification tiers
- data-lineage-tracking: Full provenance from original collection to model deployment must be tracked
© 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-training-data-class 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 Training Data Class 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 Training Data Class this skillmukul975/Privacy-Data-Protection-Skills | 295 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Compliance Testingpetrkindlmann/qa-skills | 165 | — | ~4.6k | Automated safety check: Pass | MIT | |
| Compliance Osalirezarezvani/claude-skills | 28k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Chief AI Officer Advisoralirezarezvani/claude-skills | 28k | — | ~3.5k | Automated safety check: Pass | MIT | |
| Ra Qm Skillsalirezarezvani/claude-skills | 28k | — | ~833 | Automated safety check: Pass | MIT | |
| Region Configindranilbanerjee/digital-marketing-pro | 855 | 1 repos | ~3.5k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about AI Training Data Class
What does AI Training Data Class do?
Classifies sensitive data in AI/ML training datasets including bias detection for Art. AI Training Data Class is an agent skill from mukul975/Privacy-Data-Protection-Skills. Classifies sensitive data in AI/ML training datasets including bias detection for Art.
When should I use AI Training Data Class?
AI Training Data Class fits situations like: tasks that involve Privacy and GDPR; tasks that involve Fine-tuning; tasks that involve AI governance.
How do I install AI Training Data Class in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-training-data-class -a claude-code`. Or copy the skill folder (skills/privacy/ai-training-data-class in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-training-data-class in your project. Claude Code loads it when a task matches its description.
How do I install AI Training Data Class in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-training-data-class -a codex`. Or copy the skill folder (skills/privacy/ai-training-data-class in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-training-data-class in your project. Codex loads it when a task matches its description.
Can I use AI Training Data Class 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-training-data-class -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-training-data-class, .gemini/skills/ai-training-data-class, .github/skills/ai-training-data-class and .opencode/skills/ai-training-data-class in your project.
What does AI Training Data Class need to run?
Going by SKILL.md and its folder, AI Training Data Class needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Training Data Class 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 Training Data Class 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 Training Data Class use?
AI Training Data Class 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 Training Data Class use?
About 2.9k tokens (SKILL.md is roughly 11k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to AI Training Data Class?
Skills that share tags, products or a category with AI Training Data Class: Compliance Testing (petrkindlmann/qa-skills, 165 stars), Compliance Os (alirezarezvani/claude-skills, 28k stars), Chief AI Officer Advisor (alirezarezvani/claude-skills, 28k stars) and Ra Qm Skills (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Training Data Class?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 295 GitHub stars. The repository holds 278 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.