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 GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills.
Install AI Automated Decisions
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-automated-decisions -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-automated-decisions --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-automated-decisions .claude/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .claude/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisionsType 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-automated-decisions -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-automated-decisions --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-automated-decisions .agents/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .agents/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisions -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-automated-decisions --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-automated-decisions .cursor/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .cursor/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisions--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-automated-decisions -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-automated-decisions --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-automated-decisions .gemini/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .gemini/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisionsInstalls 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-automated-decisions -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-automated-decisions .github/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .github/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisions -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-automated-decisions --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-automated-decisions .opencode/skills/ai-automated-decisions && 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-automated-decisions" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-automated-decisions into .opencode/skills/ai-automated-decisions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-automated-decisions", 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-automated-decisions- GitHub stars
- 301
- Token cost
- ~3.5k tokens
- SKILL.md length
- 1,606 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Implements GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills.
- Works in 3 steps: Right to obtain human intervention: A… → Right to express point of view: Data… → Right to contest: Formal mechanism to…
- Tasks that involve AI governance
- SKILL.md covers Overview, Art. 22 Scope and Applicability, Art. 22 Exceptions and… and Human Oversight Design — AI…, plus 5 more sections
- Runs Python scripts from its folder
What it does
AI Automated Decisions is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.
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 AI governance and 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 AI governance
- Tasks that involve Privacy and GDPR
Example prompts
- “Use the ai-automated-decisions skill to implement GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills”
- “/ai-automated-decisions”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Right to obtain human intervention: A qualified person reviews the automated decision
- Right to express point of view: Data subject can present additional information or context
- Right to contest: Formal mechanism to challenge the automated decision with review by a different decision-maker
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 Automated Decisions loads about 3.5k tokens when it runs, and up to ~7.7k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 1,606 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). 1,606 words, ~3,488 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-automated-decisions/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-automated-decisions
- description
- Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- automated-decisions, art-22, human-oversight, ai-act, profiling, contestation
AI Automated Decision-Making and Human Oversight
Overview
GDPR Article 22 grants data subjects the right not to be subject to decisions based solely on automated processing, including profiling, which produce legal or similarly significant effects. The EU AI Act Art. 14 supplements this with specific human oversight design requirements for high-risk AI systems. Together, these provisions require organisations to identify when AI systems make consequential decisions, ensure meaningful human intervention where required, provide explainable decision logic, and offer effective contestation mechanisms. This skill provides the complete framework for Art. 22 compliance and AI Act human oversight implementation.
Art. 22 Scope and Applicability
Three Cumulative Conditions
Art. 22(1) is triggered only when all three conditions are met:
| Condition | Requirement | AI Application |
|---|---|---|
| 1. Decision | A decision is made (not merely a recommendation or input) | The AI output directly determines an outcome — no genuine human decision-making step between AI output and action |
| 2. Solely automated | Based solely on automated processing including profiling | No meaningful human intervention in the decision chain; rubber-stamping does not constitute human intervention |
| 3. Legal/significant effects | Produces legal effects or similarly significantly affects the data subject | Affects legal rights, contractual status, access to services, financial outcomes, or other significant life impacts |
"Solely Automated" — EDPB Interpretation
The EDPB Guidelines 06/2020 on automated decision-making clarify:
Solely automated means no meaningful human involvement in the decision process
A human who merely confirms an AI recommendation without genuine assessment is not providing meaningful intervention
Meaningful human intervention requires:
- The reviewer has authority and competence to change the decision
- The reviewer has access to all relevant information
- Sufficient time is allocated for genuine consideration
- The reviewer routinely exercises independent judgment (not just confirming AI output)
- Override capability is actually used in practice
Not solely automated when:
- A qualified human genuinely reviews the AI output as one input among several
- The human applies independent judgment and makes the final decision
- The human has the capability and actually exercises discretion to deviate from AI recommendations
"Legal or Similarly Significant Effects"
| Category | Examples | Significance |
|---|---|---|
| Legal effects | Contract formation/termination, legal obligation imposition, legal status determination | Directly affects legal rights |
| Access to services | Denial of credit, insurance, housing, education, employment | Significantly affects life circumstances |
| Financial impact | Pricing discrimination, benefit calculation, payment terms | Material financial consequences |
| Health and safety | Medical diagnosis prioritisation, emergency response triage | Potential physical harm |
| Freedom and autonomy | Surveillance scoring, movement restriction, content blocking | Affects fundamental freedoms |
Effects that are not similarly significant (per EDPB):
- Personalised advertising (unless it reinforces prejudices about vulnerable groups)
- Generic recommendation systems (unless they determine access to information)
- Spam filtering (unless it systematically blocks important communications)
Art. 22 Exceptions and Safeguards
Art. 22(2) — Permitted Automated Decisions
| Exception | Condition | Required Safeguards |
|---|---|---|
| Art. 22(2)(a) — Contract necessity | Decision is necessary for entering into or performance of a contract | Art. 22(3) safeguards required |
| Art. 22(2)(b) — Law authorisation | Authorised by Union or Member State law with suitable measures | Law must provide suitable safeguards |
| Art. 22(2)(c) — Explicit consent | Based on explicit consent | Art. 22(3) safeguards required |
Art. 22(3) — Mandatory Safeguards
When an Art. 22(2) exception is relied upon, the controller must implement at least:
- Right to obtain human intervention: A qualified person reviews the automated decision
- Right to express point of view: Data subject can present additional information or context
- Right to contest: Formal mechanism to challenge the automated decision with review by a different decision-maker
Art. 22(4) — Special Category Data Restriction
Automated decisions based on Art. 9 special category data are only permitted under:
- Art. 9(2)(a) — Explicit consent, OR
- Art. 9(2)(g) — Substantial public interest based on Union or Member State law
In both cases, suitable measures to safeguard data subject rights must be in place.
Human Oversight Design — AI Act Art. 14
Art. 14 Requirements for High-Risk AI
High-risk AI systems must be designed and developed so that they can be effectively overseen by natural persons during use:
| Requirement | Implementation |
|---|---|
| Understand capabilities and limitations | Documentation, training, model cards |
| Monitor operation | Real-time monitoring dashboards, alert systems |
| Detect anomalies and dysfunction | Drift detection, performance monitoring |
| Interpret outputs correctly | Confidence indicators, explanation tools |
| Override or reverse decisions | Override mechanism with authority chain |
| Intervene or stop the system | Emergency stop capability |
| Be aware of automation bias | Training on automation bias, countermeasures |
Human Oversight Levels
| Level | Description | Art. 22 Compliance | Appropriate When |
|---|---|---|---|
| Human-in-the-loop (HITL) | Human reviews every AI recommendation before decision | Fully compliant if review is meaningful | High-stakes individual decisions (hiring, credit, medical) |
| Human-on-the-loop (HOTL) | Human monitors AI decisions and can intervene | Compliant if intervention capability is genuine and exercised | Medium-risk decisions with effective monitoring |
| Human-in-command (HIC) | Human sets parameters and reviews outcomes periodically | May not satisfy Art. 22 — decision is solely automated | Low-risk bulk decisions with periodic audit |
| Fully autonomous | No human oversight of individual decisions | Art. 22 applies fully — exception needed | Only where Art. 22(2) exception applies with Art. 22(3) safeguards |
Meaningful Human Intervention Criteria
A human review qualifies as "meaningful intervention" when all criteria are met:
| Criterion | Test | Red Flag |
|---|---|---|
| Authority | Reviewer has formal authority to override AI | Reviewer can only escalate, not decide |
| Competence | Reviewer has domain expertise to evaluate the decision | Reviewer is a junior staff member without training |
| Information | Reviewer has access to all inputs, the AI output, and explanation | Reviewer sees only AI score with no context |
| Time | Sufficient time allocated for genuine consideration | Reviewer processes 200+ decisions per hour |
| Independence | Reviewer exercises genuine judgment | Override rate is < 1% suggesting rubber-stamping |
| Accountability | Reviewer is accountable for the decision | Accountability rests with the AI system owner, not reviewer |
Contestation and Appeal Mechanism
Design Requirements
| Element | Requirement |
|---|---|
| Accessibility | Contestation mechanism is easy to find, access, and use |
| Timeliness | Defined response timeframe (e.g., 30 days) |
| Qualified reviewer | Different from the original decision context; has authority to overturn |
| Information provision | Data subject receives explanation of decision factors and how to contest |
| Evidence consideration | Data subject can submit additional evidence and context |
| Written outcome | Decision on contestation is documented and communicated |
| Further appeal | If contestation is denied, path to DPA complaint or judicial remedy is indicated |
Show full SKILL.md (608 more words)Show less
Contestation Workflow
- Data subject notified of automated decision with explanation
- Data subject submits contestation with reasons and any supporting evidence
- Contestation assigned to qualified human reviewer (different from original oversight)
- Reviewer assesses: AI inputs, AI output, explanation, data subject's arguments
- Reviewer makes independent decision: uphold, modify, or overturn
- Outcome communicated to data subject with reasons
- If upheld: data subject informed of further options (DPA complaint, judicial remedy)
- Record maintained in rights exercise register
AI-Specific Decision Categories
Credit and Financial Decisions
- AI application: Credit scoring, loan approval, insurance pricing, fraud detection
- Art. 22 trigger: Yes — determines access to financial services
- Required: Meaningful human review before denial; explanation of key scoring factors; contestation with independent review
- Enforcement: AEPD fined CaixaBank EUR 6M for automated credit decisions without adequate safeguards
Employment Decisions
- AI application: CV screening, candidate ranking, performance scoring, termination prediction
- Art. 22 trigger: Yes — significantly affects employment and livelihood
- Required: Human hiring manager makes final decision with genuine authority to deviate from AI ranking; applicants informed of AI use; rejected candidates can request explanation
- Enforcement: Italian DPA fined Deliveroo EUR 2.5M for algorithmic worker management without Art. 22 safeguards
Healthcare Decisions
- AI application: Diagnosis assistance, treatment recommendations, triage, risk scoring
- Art. 22 trigger: Yes if AI determines care pathway — usually mitigated by physician oversight
- Required: Physician makes final clinical decision; AI functions as decision support; patient informed of AI role
- Special category: Health data — Art. 22(4) applies
Public Administration
- AI application: Benefit eligibility, fraud detection, risk assessment, resource allocation
- Art. 22 trigger: Yes — determines access to public services and benefits
- Required: Art. 22(2)(b) legal basis required; suitable measures mandated by law; transparency about algorithmic criteria
- Enforcement: Dutch court struck down SyRI fraud detection system for lack of transparency and proportionality
Profiling Assessment
GDPR Definition of Profiling (Art. 4(4))
Any form of automated processing to evaluate personal aspects relating to a natural person, in particular to analyse or predict:
- Work performance
- Economic situation
- Health
- Personal preferences
- Interests
- Reliability
- Behaviour
- Location
- Movements
AI Profiling Risk Assessment
| Profiling Type | Risk Level | Art. 22 Trigger | Mitigation |
|---|---|---|---|
| Behavioural prediction (purchasing, browsing) | Medium | Only if decision with legal/significant effect | Opt-out, transparency |
| Credit scoring / financial risk | High | Yes — access to financial services | Human review, explanation, contestation |
| Health risk prediction | Very High | Yes — Art. 22(4) applies | Explicit consent, physician oversight |
| Criminal risk assessment | Very High | Yes — liberty and legal effects | Legal basis required, judicial oversight |
| Employment performance scoring | High | Yes — employment effects | HR human review, employee notification |
| Social scoring | Prohibited | N/A — AI Act Art. 5 prohibition | Do not implement |
Enforcement Precedents
- AEPD v. CaixaBank (PS/00421/2020, 2021): EUR 6M fine for automated credit decision-making without adequate Art. 22 safeguards, explanation, or contestation mechanism.
- Italian DPA v. Deliveroo (2021): EUR 2.5M fine for algorithmic management of delivery riders — Art. 22 applied to automated work allocation and performance scoring.
- Dutch Court v. SyRI (2020): Algorithmic fraud detection system struck down — automated profiling of citizens without proportionality, transparency, or adequate safeguards.
- Hungarian DPA v. Bank (2019): Fine for automated credit denial without providing meaningful information about decision logic or contestation mechanism.
- French Conseil d'Etat v. Parcoursup (2019): Court upheld use of algorithm for university admissions only because meaningful human review was genuinely conducted for each application.
- Austrian DPA v. CRIF (2023): Credit scoring company — violation of Art. 15(1)(h) for failing to provide meaningful information about automated scoring logic.
Integration Points
- ai-transparency-reqs: Explanation requirements feed into transparency framework
- ai-dpia: Human oversight assessment is DPIA Phase 5 component
- ai-data-subject-rights: Right to explanation and contestation are rights exercise procedures
- ai-deployment-checklist: Art. 22 compliance is pre-deployment validation item
- ai-bias-special-category: Bias in automated decisions creates Art. 22 harm
© 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-automated-decisions 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 Automated Decisions 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 Automated Decisions this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.5k | 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 |
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Related
Categories
Questions about AI Automated Decisions
What does AI Automated Decisions do?
Implements GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills. AI Automated Decisions is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements GDPR Art.
When should I use AI Automated Decisions?
AI Automated Decisions fits situations like: tasks that involve AI governance; tasks that involve Privacy and GDPR.
How do I install AI Automated Decisions in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-automated-decisions -a claude-code`. Or copy the skill folder (skills/privacy/ai-automated-decisions in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-automated-decisions in your project. Claude Code loads it when a task matches its description.
How do I install AI Automated Decisions in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-automated-decisions -a codex`. Or copy the skill folder (skills/privacy/ai-automated-decisions in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-automated-decisions in your project. Codex loads it when a task matches its description.
Can I use AI Automated Decisions 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-automated-decisions -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-automated-decisions, .gemini/skills/ai-automated-decisions, .github/skills/ai-automated-decisions and .opencode/skills/ai-automated-decisions in your project.
What does AI Automated Decisions need to run?
Going by SKILL.md and its folder, AI Automated Decisions needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Automated Decisions 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 Automated Decisions 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 Automated Decisions use?
AI Automated Decisions 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 Automated Decisions 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 4.3k tokens, read only when the agent opens those files.
What are the alternatives to AI Automated Decisions?
Skills that share tags, products or a category with AI Automated Decisions: 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 Automated Decisions?
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.