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…
Assesses AI bias risks for GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills.
Install AI Bias Special Category
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-bias-special-category -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-bias-special-category --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-bias-special-category .claude/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .claude/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-categoryType 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-bias-special-category -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-bias-special-category --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-bias-special-category .agents/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .agents/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-category -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-bias-special-category --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-bias-special-category .cursor/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .cursor/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-category--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-bias-special-category -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-bias-special-category --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-bias-special-category .gemini/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .gemini/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-categoryInstalls 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-bias-special-category -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-bias-special-category .github/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .github/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-category -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-bias-special-category --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-bias-special-category .opencode/skills/ai-bias-special-category && 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-bias-special-category" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-bias-special-category into .opencode/skills/ai-bias-special-category/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-bias-special-category", 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-bias-special-category- GitHub stars
- 301
- Token cost
- ~2.9k tokens
- SKILL.md length
- 1,236 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Assesses AI bias risks for GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills.
- Works in 3 steps: Data Audit → Model Testing → Output Analysis
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Art. 9 Special Categories and…, Fairness Metrics and Bias Detection Methodology, plus 5 more sections
- Runs Python scripts from its folder
What it does
AI Bias Special Category is an agent skill from mukul975/Privacy-Data-Protection-Skills. Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
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, AI governance and Data 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
- Tasks that involve Data governance
Example prompts
- “Use the ai-bias-special-category skill to assess AI bias risks for GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills”
- “/ai-bias-special-category”
Requirements
- Python 3
Workflow steps
3 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 Bias Special Category loads about 2.9k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 1,236 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). 1,236 words, ~2,878 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-bias-special-category/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-bias-special-category
- description
- Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- ai-bias, special-category, fairness-metrics, discrimination, art-9, data-governance
AI Bias Assessment for Special Category Data
Overview
AI systems can amplify, perpetuate, or introduce bias against protected groups defined by GDPR Art. 9 special categories (race, ethnicity, political opinion, religion, trade union membership, genetic data, biometric data, health, sexual orientation) and by EU equality law (gender, age, disability). The AI Act Art. 10 requires data governance practices for training data that address bias, while Art. 5 prohibits AI-based social scoring. This skill provides the methodology for detecting, measuring, and mitigating bias in AI systems that process or infer special category data, with documentation requirements meeting both GDPR and AI Act obligations.
Art. 9 Special Categories and AI Bias
Direct Processing of Special Category Data
When AI systems directly process Art. 9 data:
| Category | AI Bias Risk | Example |
|---|---|---|
| Racial or ethnic origin | Discrimination in hiring, credit, policing | CV screening penalising names associated with ethnic minorities |
| Political opinions | Political profiling, content suppression | News recommendation amplifying or suppressing political viewpoints |
| Religious beliefs | Service denial, discriminatory targeting | Insurance pricing varying by religious affiliation |
| Trade union membership | Employment discrimination | Performance scoring penalising union activity |
| Genetic data | Genetic discrimination in insurance/employment | Health insurance pricing based on genetic predisposition |
| Biometric data | Differential accuracy across demographics | Facial recognition with higher error rates for darker skin tones |
| Health data | Health-based discrimination | Hiring algorithms penalising disability or mental health history |
| Sexual orientation | Discrimination, outing | Content recommendation inadvertently revealing sexual orientation |
Proxy Inference of Special Categories
AI models frequently infer Art. 9 data from non-sensitive features:
| Proxy Feature | Inferred Category | Mechanism |
|---|---|---|
| Postcode/zip code | Race/ethnicity, income | Residential segregation patterns |
| First/last name | Race/ethnicity, religion | Name-ethnicity correlations |
| Browsing history | Political opinion, religion, health | Content consumption patterns |
| Purchase history | Health status, religion | Medication purchases, dietary products |
| Language patterns | National origin, education | Dialect, vocabulary, grammar patterns |
| Device/app usage | Age, income, disability | Accessibility features, device type |
EDPB position: inferring Art. 9 data from non-sensitive inputs constitutes processing of special category data — the same protections apply.
Fairness Metrics
Group Fairness Metrics
| Metric | Definition | When to Use |
|---|---|---|
| Demographic parity | P(positive outcome | group A) = P(positive outcome |
| Equalized odds | TPR and FPR equal across groups | When accuracy should be equal across groups |
| Equal opportunity | TPR equal across groups (relaxed equalized odds) | When true positive detection should be equal |
| Calibration | P(Y=1 | score=s, group=A) = P(Y=1 |
| Predictive parity | PPV equal across groups | When positive predictions should be equally reliable |
Individual Fairness Metrics
| Metric | Definition |
|---|---|
| Consistency | Similar individuals receive similar outcomes |
| Counterfactual fairness | Outcome would be the same if protected attribute were different |
| Causal fairness | No causal path from protected attribute to outcome |
Metric Selection Guidance
| Decision Context | Recommended Metric | Justification |
|---|---|---|
| Hiring/admissions | Equalized odds or equal opportunity | Equal detection of qualified candidates across groups |
| Credit scoring | Calibration | Score should mean the same probability regardless of group |
| Criminal risk | Equalized odds | Both FPR and TPR should be equal to avoid disproportionate impact |
| Healthcare | Equal opportunity + calibration | Equal detection of conditions; equal meaning of risk scores |
| Content moderation | Demographic parity | Content removal should not disproportionately affect groups |
Note: Mathematical impossibility results show that demographic parity, equalized odds, and calibration cannot all be satisfied simultaneously when base rates differ across groups. Document the trade-off explicitly.
Bias Detection Methodology
Phase 1: Data Audit
- Profile training data demographics against target population
- Identify underrepresented groups that may have lower model performance
- Check for historical bias in labelled data (e.g., biased hiring decisions used as ground truth)
- Identify label noise differential across groups
- Assess feature distributions across groups for proxy discrimination potential
Phase 2: Model Testing
- Evaluate model performance disaggregated by protected groups
- Compute selected fairness metrics for each protected group pair
- Run counterfactual testing: change protected attributes, observe output changes
- Test for intersectional bias (combinations of protected attributes)
- Evaluate model performance on edge cases and adversarial examples per group
Phase 3: Output Analysis
- Analyse decision distribution across groups
- Identify threshold effects that disproportionately impact specific groups
- Test for feedback loop amplification over time
- Assess output explanation fairness (are explanations equally informative across groups?)
Bias Mitigation Strategies
Pre-processing (Data-Level)
| Strategy | Description | Trade-off |
|---|---|---|
| Resampling | Over-sample underrepresented groups, under-sample overrepresented | May reduce data diversity or introduce duplicates |
| Reweighting | Assign higher weights to underrepresented group samples | Computationally simple; may not address structural bias |
| Relabelling | Correct historically biased labels | Requires domain expertise; may be subjective |
| Fair representation learning | Learn latent representation that removes protected attribute information | May lose legitimate correlations |
Show full SKILL.md (485 more words)Show less
In-processing (Algorithm-Level)
| Strategy | Description | Trade-off |
|---|---|---|
| Adversarial debiasing | Train adversary to predict protected attribute from model; penalise success | Accuracy-fairness trade-off; requires protected attribute data |
| Fairness constraints | Add fairness metric as training constraint | May reduce overall accuracy; constraint satisfaction varies |
| Regularisation | Add fairness-related regularisation term to loss function | Balances accuracy and fairness; requires tuning |
| Causal modelling | Use causal graph to block discriminatory paths | Requires causal knowledge; complex to implement |
Post-processing (Output-Level)
| Strategy | Description | Trade-off |
|---|---|---|
| Threshold adjustment | Different decision thresholds per group to equalise metrics | May be perceived as unfair; legally complex |
| Score calibration | Calibrate scores per group | Requires sufficient group data; may reduce discrimination |
| Reject option | Abstain from decision for borderline cases across groups | Reduces coverage; requires human fallback |
AI Act Art. 10 Data Governance
Art. 10 requires for high-risk AI training data:
| Requirement | Implementation |
|---|---|
| Relevant data | Training data must be relevant to the intended purpose |
| Sufficiently representative | Data must represent the population the system will be deployed on |
| Free of errors | Data quality assessment and cleaning processes |
| Complete | Sufficient coverage of deployment scenarios |
| Appropriate statistical properties | Distribution analysis including demographic representation |
| Bias examination | Examine training data for possible biases, especially related to Art. 10(2)(f) |
Art. 10(5): Processing of special category data for bias detection is permitted for high-risk AI if:
- Strictly necessary for bias monitoring, detection, and correction
- Subject to appropriate safeguards (pseudonymisation, access controls)
- Data is not used for other purposes
- Deleted after bias assessment unless retention is required for compliance documentation
Documentation Requirements
Bias Assessment Report
| Section | Content |
|---|---|
| System description | Model, purpose, affected groups |
| Protected attributes assessed | Art. 9 categories + equality law characteristics |
| Fairness metrics selected | With justification for selection |
| Data audit results | Training data demographics, representation gaps |
| Model testing results | Per-group performance, fairness metrics, counterfactual results |
| Bias findings | Identified disparities with severity assessment |
| Mitigation measures | Applied strategies with effectiveness evidence |
| Residual bias | Remaining disparities after mitigation |
| Trade-off documentation | Accuracy-fairness trade-offs, metric impossibility acknowledgement |
| Ongoing monitoring plan | Post-deployment fairness monitoring |
Enforcement Precedents
- Dutch Tax Authority (SyRI, 2020): Court struck down algorithmic fraud detection for discriminatory profiling — system disproportionately targeted residents of low-income, immigrant-background neighbourhoods.
- Italian DPA v. Deliveroo (2021): Algorithmic worker management found to discriminate based on protected characteristics — Art. 22 and equality law violations.
- Austrian DPA v. AMS Algorithm (2020): Austrian employment service algorithm that scored job seekers lower based on gender, age, disability, and citizenship — DPA found processing unlawful.
- AEPD guidance (2021): Spanish DPA issued guidance on algorithmic discrimination, emphasising DPIA requirement for AI systems processing protected characteristics.
- CNIL (2024): AI bias assessment framework published requiring fairness testing for high-impact AI systems.
Integration Points
- ai-dpia: Bias assessment feeds into DPIA risk assessment for discrimination harms
- ai-automated-decisions: Biased automated decisions trigger Art. 22 and equality law violations
- ai-training-lawfulness: Art. 10(5) processing of special category data for bias detection requires documentation
- ai-transparency-reqs: Bias findings should be disclosed in model cards and transparency documentation
- ai-act-high-risk-docs: Art. 10 data governance documentation is part of conformity assessment
© 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-bias-special-category 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 Bias Special Category 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 Bias Special Category this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~2.9k | 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 | |
| Implementing Cloud Dlp For Data Protectionmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| 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 |
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Related
Categories
Questions about AI Bias Special Category
What does AI Bias Special Category do?
Assesses AI bias risks for GDPR Art. An agent skill from mukul975/Privacy-Data-Protection-Skills. AI Bias Special Category is an agent skill from mukul975/Privacy-Data-Protection-Skills. Assesses AI bias risks for GDPR Art.
When should I use AI Bias Special Category?
AI Bias Special Category fits situations like: tasks that involve Privacy and GDPR; tasks that involve AI governance; tasks that involve Data governance.
How do I install AI Bias Special Category in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-bias-special-category -a claude-code`. Or copy the skill folder (skills/privacy/ai-bias-special-category in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-bias-special-category in your project. Claude Code loads it when a task matches its description.
How do I install AI Bias Special Category in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-bias-special-category -a codex`. Or copy the skill folder (skills/privacy/ai-bias-special-category in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-bias-special-category in your project. Codex loads it when a task matches its description.
Can I use AI Bias Special Category 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-bias-special-category -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-bias-special-category, .gemini/skills/ai-bias-special-category, .github/skills/ai-bias-special-category and .opencode/skills/ai-bias-special-category in your project.
What does AI Bias Special Category need to run?
Going by SKILL.md and its folder, AI Bias Special Category needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Bias Special Category 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 Bias Special Category 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 Bias Special Category use?
AI Bias Special Category 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 Bias Special Category use?
About 2.9k 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 1.6k tokens, read only when the agent opens those files.
What are the alternatives to AI Bias Special Category?
Skills that share tags, products or a category with AI Bias Special Category: Compliance Testing (petrkindlmann/qa-skills, 170 stars), Compliance Os (alirezarezvani/claude-skills, 28k stars), Implementing Cloud Dlp For Data Protection (mukul975/Anthropic-Cybersecurity-Skills, 34k 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 Bias Special Category?
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.