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Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art.
Install AI Privacy Inference
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-privacy-inference -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-privacy-inference --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-privacy-inference .claude/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .claude/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inferenceType 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-privacy-inference -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-privacy-inference --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-privacy-inference .agents/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .agents/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inference -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-privacy-inference --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-privacy-inference .cursor/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .cursor/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inference--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-privacy-inference -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-privacy-inference --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-privacy-inference .gemini/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .gemini/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inferenceInstalls 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-privacy-inference -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-privacy-inference .github/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .github/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inference -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-privacy-inference --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-privacy-inference .opencode/skills/ai-privacy-inference && 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-privacy-inference" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-privacy-inference into .opencode/skills/ai-privacy-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-privacy-inference", 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-privacy-inference- GitHub stars
- 301
- Token cost
- ~3.5k tokens
- SKILL.md length
- 1,636 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art.
- Works in 4 steps: Use appropriate mathematical or… → Implement appropriate technical and… → Correct inaccuracies and enable… → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Legal Framework for AI…, Profiling Under GDPR Article 22 and Inference Accuracy and Quality…, plus 4 more sections
- Runs Python scripts from its folder
What it does
AI Privacy Inference is an agent skill from mukul975/Privacy-Data-Protection-Skills. Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
Its SKILL.md is about 3.5k 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 Data analysis. 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 Data analysis
Example prompts
- “/ai-privacy-inference”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Use appropriate mathematical or statistical procedures for profiling
- Implement appropriate technical and organisational measures to minimise the risk of errors
- Correct inaccuracies and enable rectification
- Secure personal data to prevent discriminatory effects
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 Privacy Inference loads about 3.5k tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 1,636 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). 1,636 words, ~3,495 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-privacy-inference/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-privacy-inference
- description
- Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- ai-inference, derived-data, profiling, automated-predictions, gdpr-art-22
AI Privacy Inference and Derived Data
Overview
AI systems routinely generate inferences about individuals — predictions about creditworthiness, health risks, personality traits, political opinions, or behavioural patterns that were never directly provided by the data subject. These AI-derived inferences raise critical privacy questions: Are inferences personal data? When does inference become profiling under GDPR Article 22? What accuracy obligations apply to AI predictions? Can data subjects access, rectify, or object to inferences drawn about them? The CJEU, EDPB, and national DPAs have progressively clarified that inferences are personal data when they relate to an identified or identifiable person, and that GDPR rights extend to derived and inferred data. Cerebrum AI Labs must classify, govern, and provide transparency over all inferences its AI systems generate about individuals.
Legal Framework for AI Inferences
When Inferences Are Personal Data
| Criterion | Analysis | Example |
|---|---|---|
| Relates to an identified person | Inference is linked to a specific customer record or user profile | "Customer C-12345 has 78% churn probability" |
| Relates to an identifiable person | Inference can be linked to a person through combination with other data | "User with session token X-789 is likely aged 25-34" |
| Used to evaluate a person | Inference is used to assess, classify, or make decisions about someone | Credit score derived from transaction patterns |
| Has impact on a person | Inference affects how the person is treated or what options are available | Insurance premium adjusted based on predicted health risk |
CJEU C-434/16 (Nowak, 2017): Personal data includes "any information" relating to a data subject — this encompasses opinions, assessments, and inferences, not only factual data directly provided by the individual.
EDPB Guidelines 8/2020 on Targeting of Social Media Users: Inferred data (data created by the controller through observation or derivation) constitutes personal data and is subject to the full scope of GDPR rights.
GDPR Classification of Inference Types
| Inference Type | Classification | GDPR Implications |
|---|---|---|
| Observed data | Data collected through direct interaction (browsing history, purchase records) | Standard personal data — Art. 6 lawful basis required |
| Derived data | Data created by the controller through computation on existing data (credit score, risk rating) | Personal data — subject to access, rectification, objection rights |
| Inferred data | Probabilistic predictions about characteristics not directly observed (personality, health risk) | Personal data — potentially special category if predicting Art. 9 characteristics |
| Aggregated data | Statistical outputs at group level, not linked to individuals | Not personal data if truly anonymous (k-anonymity verified) |
When Inferences Become Special Category Data
| Predicted Characteristic | Art. 9 Category | Trigger |
|---|---|---|
| Ethnic origin from name/location patterns | Racial or ethnic origin | Any inference about ethnic background, even probabilistic |
| Political leaning from content engagement | Political opinions | Prediction used to classify or target based on politics |
| Religious affiliation from purchase patterns | Religious beliefs | Halal/kosher purchase scoring, prayer time activity patterns |
| Health condition from behavioural signals | Health data | Step count decline predicting depression, typing pattern analysis |
| Sexual orientation from browsing/social data | Sexual orientation | Any inference about sexual orientation regardless of accuracy |
| Pregnancy from purchase pattern shifts | Health data + gender | Purchase category analysis predicting pregnancy status |
Cerebrum AI Labs Policy: Any inference that predicts, estimates, or classifies an Art. 9 characteristic — even indirectly or probabilistically — must be treated as special category data and requires an Art. 9(2) condition for processing.
Profiling Under GDPR Article 22
Profiling Definition (Art. 4(4))
Profiling means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning:
- Work performance
- Economic situation
- Health
- Personal preferences
- Interests
- Reliability
- Behaviour
- Location
- Movements
Three-Level Framework
| Level | Description | GDPR Requirement | Cerebrum AI Labs Example |
|---|---|---|---|
| Profiling only | Automated evaluation without decision | Art. 6 lawful basis + Art. 13-14 transparency | Customer segmentation for analytics dashboard |
| Profiling + human decision | Automated evaluation informing a human decision-maker | Art. 6 lawful basis + transparency + meaningful human involvement | Credit risk score reviewed by loan officer |
| Solely automated decision with legal/significant effects | Automated decision with no meaningful human involvement producing legal or similarly significant effects | Art. 22(1) prohibition applies — must fall within Art. 22(2) exceptions | Automated loan rejection based solely on AI credit score |
Art. 22(2) Exceptions Allowing Solely Automated Decisions
| Exception | Requirement | Cerebrum AI Labs Application |
|---|---|---|
| Art. 22(2)(a) — Contract | Decision necessary for entering into or performing a contract | Automated credit pre-approval for existing customers |
| Art. 22(2)(b) — Law | Authorised by EU or Member State law with suitable safeguards | Regulatory-mandated fraud screening |
| Art. 22(2)(c) — Explicit consent | Data subject's explicit consent obtained | Customer opts in to automated portfolio rebalancing |
Safeguards Required (Art. 22(3))
| Safeguard | Implementation at Cerebrum AI Labs |
|---|---|
| Right to obtain human intervention | Escalation button in customer portal routes to trained human reviewer within 2 business days |
| Right to express point of view | Customer can submit additional context through contestation form before human review |
| Right to contest the decision | Appeal process with independent review panel; decision reversed if AI error demonstrated |
| Right to explanation | Individual explanation generated using SHAP values showing top 5 factors influencing the AI decision |
Inference Accuracy and Quality Obligations
GDPR Article 5(1)(d) — Accuracy Principle
AI inferences must be accurate, and where necessary, kept up to date. For probabilistic predictions this means:
| Obligation | Implementation |
|---|---|
| Accuracy measurement | Track prediction accuracy metrics (precision, recall, F1) per demographic group |
| Confidence thresholds | Do not present inferences with confidence below 70% as actionable without human review |
| Staleness detection | Re-evaluate inferences when underlying data changes; flag inferences older than 90 days |
| Accuracy disclosure | Inform data subjects of the probabilistic nature and known accuracy of inferences |
| Rectification of inferences | Allow data subjects to challenge inferences; if input data is corrected, regenerate inference |
EDPB Position on Inference Accuracy
The EDPB Guidelines on Automated Decision-Making (WP 251 rev.01) state that controllers must:
- Use appropriate mathematical or statistical procedures for profiling
- Implement appropriate technical and organisational measures to minimise the risk of errors
- Correct inaccuracies and enable rectification
- Secure personal data to prevent discriminatory effects
Transparency Requirements for AI Inferences
Show full SKILL.md (663 more words)Show less
Art. 13-14 Information Requirements
| Information | Requirement | Cerebrum AI Labs Implementation |
|---|---|---|
| Existence of profiling | Inform data subjects that profiling occurs | Privacy notice section: "How we use AI to analyse your data" |
| Logic involved | Meaningful information about the logic involved | Technical explainer: "Our AI analyses your transaction patterns, account tenure, and product usage to predict service needs" |
| Significance | Envisaged consequences of such processing | "This analysis may affect the products and offers shown to you, and may influence credit decisions" |
| Categories of data used | What data feeds the inference | "We use: transaction history, account tenure, product holdings, service interactions" |
| Inference outputs | What inferences are generated | "We generate: churn probability, product affinity scores, credit risk indicators" |
AI Act Transparency Requirements
| Obligation | AI Act Article | Cerebrum AI Labs Implementation |
|---|---|---|
| Inform users they are interacting with AI | Art. 52(1) | Chat interface disclosure: "You are interacting with an AI assistant" |
| Disclose AI-generated content | Art. 52(3) | Outputs marked: "This recommendation was generated by AI" |
| Disclose emotion recognition | Art. 52(2) | Not applicable — Cerebrum AI Labs does not use emotion recognition |
| High-risk system deployer transparency | Art. 13 | Technical documentation available to regulators on request |
Inference Governance Framework
Cerebrum AI Labs Inference Registry
All AI systems that generate inferences about individuals must be registered in the Cerebrum AI Labs Inference Registry:
| Registry Field | Description |
|---|---|
| System ID | Unique identifier for the AI system |
| Inference type | Category of inference generated (behavioural, demographic, financial, health) |
| Data subjects affected | Categories and approximate volume of individuals profiled |
| Input features | Data elements used to generate the inference |
| Output format | Inference output (score, category, probability, ranking) |
| Accuracy metrics | Latest precision, recall, F1 by demographic group |
| Retention period | How long inferences are stored before deletion |
| Art. 22 assessment | Whether the inference feeds a solely automated decision |
| Lawful basis | Art. 6 and (if applicable) Art. 9 basis for generating the inference |
| DPIA reference | Associated DPIA document ID |
Inference Lifecycle Controls
| Phase | Control |
|---|---|
| Generation | Log all inferences with timestamp, model version, confidence score, input data hash |
| Storage | Encrypt inference outputs; apply access controls limiting who can read individual-level inferences |
| Usage | Track downstream consumption of inferences; prevent scope creep beyond documented purposes |
| Disclosure | Make inferences available to data subjects on request (Art. 15 access right) |
| Rectification | If underlying data is corrected, flag dependent inferences for regeneration |
| Deletion | Delete inferences per retention schedule (90 days operational, 12 months audit) |
Enforcement and Regulatory Precedents
- CJEU C-634/21 (SCHUFA, 2023): The CJEU held that the generation of a credit score by a private entity constitutes "automated individual decision-making" under Art. 22 if the score plays a decisive role in a subsequent decision by a third party. This means AI-derived scores used in downstream decisions may trigger Art. 22 protections even if the AI provider is not the final decision-maker.
- Austrian DPA — Case DSB-D550.038 (2022): Found that inferred political opinions from social media activity constitute special category data under Art. 9, even when the inference is probabilistic and may be inaccurate.
- Norwegian DPA — Grindr Decision (2021): NOK 65 million fine for sharing data enabling inference of sexual orientation. Established that data enabling inference of Art. 9 characteristics is itself special category data.
- CNIL — Clearview AI (2022): EUR 20 million fine, finding that biometric inferences (face embeddings) from public photographs are personal data subject to full GDPR compliance.
Key Legal References
- GDPR Article 4(4) — Definition of profiling
- GDPR Article 5(1)(d) — Accuracy principle
- GDPR Article 13(2)(f) — Right to information about profiling logic and significance
- GDPR Article 15(1)(h) — Right of access to profiling information
- GDPR Article 22 — Automated individual decision-making including profiling
- GDPR Recital 71 — Safeguards for profiling and automated decisions
- GDPR Recital 72 — Guidelines on profiling
- CJEU C-434/16 (Nowak, 2017) — Broad interpretation of personal data including assessments
- CJEU C-634/21 (SCHUFA, 2023) — Credit scoring as automated decision-making
- EDPB Guidelines on Automated Decision-Making (WP 251 rev.01) — Art. 22 interpretation
- EDPB Guidelines 8/2020 on Targeting of Social Media Users — Inferred data as personal data
- EU AI Act Article 52 — Transparency obligations for AI systems
- EU AI Act Article 14 — Human oversight for high-risk AI systems
© 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-privacy-inference 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 Privacy Inference 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 Privacy Inference this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.5k | 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 Privacy Inference
What does AI Privacy Inference do?
Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. AI Privacy Inference is an agent skill from mukul975/Privacy-Data-Protection-Skills. Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art.
When should I use AI Privacy Inference?
AI Privacy Inference fits situations like: tasks that involve Privacy and GDPR; tasks that involve Data analysis.
How do I install AI Privacy Inference in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-privacy-inference -a claude-code`. Or copy the skill folder (skills/privacy/ai-privacy-inference in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-privacy-inference in your project. Claude Code loads it when a task matches its description.
How do I install AI Privacy Inference in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-privacy-inference -a codex`. Or copy the skill folder (skills/privacy/ai-privacy-inference in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-privacy-inference in your project. Codex loads it when a task matches its description.
Can I use AI Privacy Inference 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-privacy-inference -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-privacy-inference, .gemini/skills/ai-privacy-inference, .github/skills/ai-privacy-inference and .opencode/skills/ai-privacy-inference in your project.
What does AI Privacy Inference need to run?
Going by SKILL.md and its folder, AI Privacy Inference needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Privacy Inference 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 Privacy Inference 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 Privacy Inference use?
AI Privacy Inference 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 Privacy Inference use?
About 3.5k 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 3.3k tokens, read only when the agent opens those files.
What are the alternatives to AI Privacy Inference?
Skills that share tags, products or a category with AI Privacy Inference: 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 Privacy Inference?
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.