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Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis.
Install Biometric Dpia
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill biometric-dpia -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills biometric-dpia --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/biometric-dpia .claude/skills/biometric-dpia && 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 "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .claude/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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/biometric-dpiaType 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 biometric-dpia -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills biometric-dpia --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/biometric-dpia .agents/skills/biometric-dpia && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .agents/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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 biometric-dpia -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills biometric-dpia --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/biometric-dpia .cursor/skills/biometric-dpia && 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 "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .cursor/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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/biometric-dpia--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 biometric-dpia -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills biometric-dpia --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/biometric-dpia .gemini/skills/biometric-dpia && 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 "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .gemini/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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 biometric-dpiaInstalls 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 biometric-dpia -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/biometric-dpia .github/skills/biometric-dpia && 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 "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .github/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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 biometric-dpia -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 biometric-dpia --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/biometric-dpia .opencode/skills/biometric-dpia && 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 "biometric-dpia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/biometric-dpia into .opencode/skills/biometric-dpia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biometric-dpia", 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
biometric-dpia- GitHub stars
- 301
- Token cost
- ~3.2k tokens
- SKILL.md length
- 1,449 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis.
- Large-scale biometric processing
- SKILL.md covers Overview, Legal Framework for Biometric…, Biometric System Types and… and DPIA Content for Biometric…, plus 3 more sections
- Runs Python scripts from its folder
- EDPB Guidelines 3/2019 on video surveillance
What it does
Biometric Dpia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB Guidelines 3/2019 on video surveillance. Keywords: biometric, facial recognition, fingerprint, DPIA, Art. 9, special category, EDPB Guidelines 3/2019.
Its SKILL.md is about 3.2k 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. 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
- Large-scale biometric processing
- EDPB Guidelines 3/2019 on video surveillance
Example prompts
- “Use the biometric-dpia skill to guide DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis”
- “/biometric-dpia”
Requirements
- Python 3
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
Biometric Dpia loads about 3.2k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 1,449 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). 1,449 words, ~3,180 tokens.
Download SKILL.mdSave it as
.claude/skills/biometric-dpia/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- biometric-dpia
- description
- Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB Guidelines 3/2019 on video surveillance. Keywords: biometric, facial recognition, fingerprint, DPIA, Art. 9, special category, EDPB Guidelines 3/2019.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- privacy-impact-assessment
- metadata.tags
- biometric, facial-recognition, fingerprint, dpia, art-9, special-category
Assessing Biometric Processing Privacy
Overview
Biometric data is classified as a special category of personal data under GDPR Art. 9(1) when processed for the purpose of uniquely identifying a natural person. Processing biometric data on a large scale triggers a mandatory DPIA under Art. 35(3)(b). This skill provides a comprehensive DPIA methodology for biometric systems including facial recognition, fingerprint identification, voice recognition, iris scanning, vein pattern analysis, and behavioural biometrics (gait, typing patterns, signature dynamics).
Legal Framework for Biometric Data
GDPR Definition — Art. 4(14)
"'Biometric data' means personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic data."
Art. 9(1) — Prohibition on Processing Special Categories
Processing of biometric data for the purpose of uniquely identifying a natural person is prohibited unless one of the Art. 9(2) exemptions applies.
Critical distinction: Art. 9 only applies when biometric data is processed "for the purpose of uniquely identifying" a person. A photograph used for illustration purposes is not Art. 9 data; the same photograph processed through facial recognition software to identify the person is Art. 9 data.
Art. 9(2) Exemptions Applicable to Biometric Processing
| Exemption | Reference | Application to Biometrics |
|---|---|---|
| Explicit consent | Art. 9(2)(a) | Employee consent often not freely given due to power imbalance (WP29 Opinion 2/2017). Consumer biometric consent must meet Art. 7 standards. |
| Employment, social security, social protection law | Art. 9(2)(b) | Member State law may authorise biometric processing in the employment context (e.g., biometric access control for high-security areas). |
| Vital interests | Art. 9(2)(c) | Limited to emergency situations where biometric identification is needed to protect someone's life. |
| Substantial public interest | Art. 9(2)(g) | Member State law basis required. May apply to law enforcement biometrics where authorised by specific legislation. |
| Health or social care | Art. 9(2)(h) | Biometric patient identification in healthcare settings. |
| Public health | Art. 9(2)(i) | Biometric contact tracing during health emergencies (subject to proportionality). |
| Archiving, scientific research, statistics | Art. 9(2)(j) | Biometric research (e.g., medical imaging analysis) with Art. 89(1) safeguards. |
Art. 35(3)(b) — Mandatory DPIA Trigger
Processing on a large scale of special categories of data referred to in Art. 9(1), including biometric data processed for unique identification, requires a DPIA. "Large scale" factors per WP248rev.01:
- Number of data subjects (in absolute terms or as a proportion of the relevant population)
- Volume of data and/or range of data items
- Duration or permanence of the processing
- Geographic scope
EDPB Guidelines 3/2019 on Video Surveillance
Key provisions relevant to facial recognition CCTV:
- Facial recognition in public spaces generally constitutes systematic monitoring of publicly accessible areas (Art. 35(3)(c)) in addition to large-scale biometric processing (Art. 35(3)(b)).
- Facial recognition for access control is less intrusive than identification in public spaces but still requires DPIA.
- Purpose limitation: biometric templates created for access control must not be repurposed for attendance monitoring or performance management.
- Storage limitation: biometric templates should be stored on a device controlled by the data subject (e.g., access card) rather than a central database where possible.
Biometric System Types and Risk Assessment
Verification (1:1 Matching)
One-to-one comparison of a live biometric sample against a stored template for the claimed identity. Used for access control, device unlock, payment authentication.
| Risk Factor | Assessment |
|---|---|
| Data subjects | Defined, enrolled individuals |
| Volume of data | Limited to enrolled population |
| Proportionality | Generally more proportionate than identification |
| Storage recommendation | Template stored on user's device or card (decentralised) |
| Art. 35(3)(b) trigger | Depends on scale of enrolled population |
Identification (1:N Matching)
One-to-many comparison of a live biometric sample against a database of templates to determine identity. Used for law enforcement, border control, surveillance.
| Risk Factor | Assessment |
|---|---|
| Data subjects | Potentially unlimited (all persons in the capture area) |
| Volume of data | Can be very large (entire population databases) |
| Proportionality | Highly intrusive; requires strong justification |
| Storage recommendation | Centralised database is typically required for 1:N matching |
| Art. 35(3)(b) trigger | Almost always triggered |
Categorisation
Classification of individuals into groups based on biometric characteristics (age, gender, ethnicity, emotion) without uniquely identifying them. Used for analytics, targeted advertising, audience measurement.
| Risk Factor | Assessment |
|---|---|
| Art. 9 applicability | May not fall under Art. 9 if not used for unique identification, but still high risk |
| AI Act classification | Emotion recognition in workplace/education prohibited (Art. 5(1)(f)) |
| Discrimination risk | Categorisation by race, ethnicity, or emotion raises equality law concerns |
DPIA Content for Biometric Systems
Systematic Description
For biometric systems, the Art. 35(7)(a) systematic description must include:
| Element | Required Detail |
|---|---|
| Biometric modality | Facial, fingerprint, iris, voice, vein, gait, or multi-modal |
| Processing mode | Verification (1:1) or identification (1:N) or categorisation |
| Capture environment | Controlled (sensor/scanner) or uncontrolled (CCTV, ambient camera) |
| Template storage | Centralised database, decentralised (user device/card), or encrypted enclave |
| Template format | Proprietary template, ISO 19794 standard, or raw biometric data |
| Matching algorithm | Vendor algorithm (specify), open-source algorithm, or custom development |
| Accuracy metrics | False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) |
| Liveness detection | Anti-spoofing measures (presentation attack detection) |
| Fallback mechanism | Alternative identification method when biometric fails |
| Retention period | Template retention, raw biometric data retention, audit log retention |
Show full SKILL.md (615 more words)Show less
Necessity and Proportionality for Biometric Processing
The proportionality assessment for biometric systems must be rigorous because biometric data:
- Is permanent (unlike passwords, biometric characteristics cannot be changed if compromised)
- Is uniquely identifying (biometric data is inherently linked to the individual)
- Creates heightened risk of function creep (biometric templates can be repurposed)
- Has disproportionate impact if breached (biometric data cannot be reissued)
| Proportionality Question | Assessment Standard |
|---|---|
| Is biometric processing necessary, or can a non-biometric alternative achieve the same purpose? | Badge/card access, PIN, password, or multi-factor authentication without biometrics |
| Is the biometric modality the least intrusive option? | Fingerprint is generally less intrusive than facial recognition; on-device verification less intrusive than centralised identification |
| Is the scale of biometric processing proportionate? | Processing all persons in an area (identification) is less proportionate than processing enrolled volunteers (verification) |
| Is the retention of biometric data minimised? | On-card template storage preferred over centralised database; raw biometric images should not be retained after template extraction |
Risk Register for Biometric DPIA
Common Biometric Processing Risks
| Risk ID | Risk | Likelihood | Severity | Typical Level |
|---|---|---|---|---|
| BIO-R1 | Biometric data breach — templates or raw data exposed to unauthorised parties | Possible | Maximum | Very High |
| BIO-R2 | Function creep — biometric data collected for access control repurposed for surveillance or attendance monitoring | Likely | Significant | High |
| BIO-R3 | Discriminatory accuracy — facial recognition performs worse on certain demographic groups (skin colour, age, gender) | Likely | Significant | High |
| BIO-R4 | False rejection denying legitimate access — disabled individuals, elderly, or those with skin conditions experience higher rejection rates | Possible | Significant | High |
| BIO-R5 | Spoofing or presentation attacks — fraudulent biometric samples (photos, masks, artificial fingerprints) bypass security | Possible | Significant | High |
| BIO-R6 | Chilling effect — knowledge of biometric surveillance alters behaviour in public or workplace spaces | Likely | Limited | High |
| BIO-R7 | Irreversibility — unlike passwords, compromised biometric data cannot be changed or reissued | Almost certain | Maximum | Very High |
| BIO-R8 | Third-party capture — biometric data of non-enrolled individuals incidentally captured by the system | Likely | Limited | High |
Mitigation Measures for Biometric Processing
| Measure | Type | Risk Addressed |
|---|---|---|
| On-device or on-card template storage (no centralised database) | Technical (DPbD) | BIO-R1, BIO-R7 |
| Template protection: cancellable biometrics or biometric encryption (BioHashing, fuzzy vault) | Technical | BIO-R1, BIO-R7 |
| Liveness detection / presentation attack detection (ISO 30107 compliance) | Technical | BIO-R5 |
| Purpose limitation enforcement through technical access controls | Technical + Organisational | BIO-R2 |
| Demographic accuracy testing across skin colour, age, gender groups (NIST FRVT benchmarks) | Technical | BIO-R3 |
| Non-biometric fallback mechanism (PIN, card, helpdesk override) | Organisational | BIO-R4 |
| Signage and transparency notices in capture areas | Organisational (Transparency) | BIO-R6, BIO-R8 |
| Automatic deletion of raw biometric images after template extraction | Technical (Data minimisation) | BIO-R1 |
| Regular penetration testing of biometric system | Technical (Security) | BIO-R5 |
Enforcement Precedents
- CNIL vs Clearview AI (2022): EUR 20 million fine for mass collection of facial images from internet for biometric identification database without consent, transparency, or DPIA.
- ICO vs Clearview AI (2022): GBP 7.5 million fine for same biometric processing; enforcement notice ordering deletion of UK residents' biometric data.
- Swedish DPA vs Skelleftea Municipality (2019): SEK 200,000 fine for school using facial recognition for student attendance tracking. Consent relied upon was not freely given due to power imbalance. Less intrusive alternatives were available.
- ICO vs Serco Leisure (2022): Enforcement notice for requiring employees to use facial recognition for time and attendance at leisure centres. No DPIA conducted; no less intrusive alternative offered.
- CNIL vs Clearview AI (2023): EUR 5.2 million additional penalty for non-compliance with the 2022 order, demonstrating the consequences of continued biometric processing in violation of enforcement.
- French Conseil d'Etat vs Presto (2020): Upheld CNIL enforcement against biometric time-and-attendance system for employees; found that fingerprint scanning for attendance was disproportionate when badge systems were available.
- Italian Garante vs P&G (Gillette) (2022): Enforcement action against automated age estimation system using facial analysis in retail stores without DPIA and without valid consent.
© 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/biometric-dpia 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
Biometric Dpia 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 |
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| Biometric Dpia this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.2k | 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 Biometric Dpia
What does Biometric Dpia do?
Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Biometric Dpia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis.
When should I use Biometric Dpia?
Biometric Dpia fits situations like: large-scale biometric processing; EDPB Guidelines 3/2019 on video surveillance.
How do I install Biometric Dpia in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill biometric-dpia -a claude-code`. Or copy the skill folder (skills/privacy/biometric-dpia in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/biometric-dpia in your project. Claude Code loads it when a task matches its description.
How do I install Biometric Dpia in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill biometric-dpia -a codex`. Or copy the skill folder (skills/privacy/biometric-dpia in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/biometric-dpia in your project. Codex loads it when a task matches its description.
Can I use Biometric Dpia 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 biometric-dpia -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/biometric-dpia, .gemini/skills/biometric-dpia, .github/skills/biometric-dpia and .opencode/skills/biometric-dpia in your project.
What does Biometric Dpia need to run?
Going by SKILL.md and its folder, Biometric Dpia needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Biometric Dpia 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 Biometric Dpia 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 Biometric Dpia use?
Biometric Dpia 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 Biometric Dpia use?
About 3.2k tokens (SKILL.md is roughly 13k 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 3.1k tokens, read only when the agent opens those files.
What are the alternatives to Biometric Dpia?
Skills that share tags, products or a category with Biometric Dpia: 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 Biometric Dpia?
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.