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Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization.
Install Anonymization Alternative
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill anonymization-alternative -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills anonymization-alternative --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/anonymization-alternative .claude/skills/anonymization-alternative && 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 "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .claude/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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/anonymization-alternativeType 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 anonymization-alternative -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills anonymization-alternative --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/anonymization-alternative .agents/skills/anonymization-alternative && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .agents/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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 anonymization-alternative -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills anonymization-alternative --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/anonymization-alternative .cursor/skills/anonymization-alternative && 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 "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .cursor/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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/anonymization-alternative--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 anonymization-alternative -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills anonymization-alternative --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/anonymization-alternative .gemini/skills/anonymization-alternative && 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 "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .gemini/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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 anonymization-alternativeInstalls 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 anonymization-alternative -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/anonymization-alternative .github/skills/anonymization-alternative && 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 "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .github/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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 anonymization-alternative -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 anonymization-alternative --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/anonymization-alternative .opencode/skills/anonymization-alternative && 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 "anonymization-alternative" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/anonymization-alternative into .opencode/skills/anonymization-alternative/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anonymization-alternative", 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
anonymization-alternative- GitHub stars
- 295
- Token cost
- ~3.8k tokens
- SKILL.md length
- 1,233 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 278
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization.
- Works in 4 steps: Determine Whether Anonymization is… → Select Anonymization Technique → Implement Anonymization → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Legal Foundation, Anonymization vs.… and WP29 Anonymization Techniques, plus 3 more sections
- Runs Python scripts from its folder
What it does
Anonymization Alternative is an agent skill from mukul975/Privacy-Data-Protection-Skills. Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.
Its SKILL.md is about 3.8k 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
- Tasks that involve Privacy and GDPR
Example prompts
- “Use the anonymization-alternative skill to evaluate anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion…”
- “/anonymization-alternative”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 9b2ef9e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Anonymization Alternative loads about 3.8k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 1,233 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). 1,233 words, ~3,756 tokens.
Download SKILL.mdSave it as
.claude/skills/anonymization-alternative/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- anonymization-alternative
- description
- Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- data-retention-deletion
- metadata.tags
- anonymization, de-identification, k-anonymity, wp29-opinion, recital-26
Anonymization as Retention Alternative
Overview
Anonymization transforms personal data into a form that no longer identifies or can reasonably be used to identify a natural person. Under GDPR Recital 26, truly anonymized data falls outside the scope of the regulation, meaning it can be retained indefinitely without a legal basis, without data subject rights applying, and without counting toward retention period obligations. However, achieving genuine anonymization — as opposed to mere pseudonymization — requires rigorous application of techniques validated against re-identification risk. This skill provides the assessment framework, implementation techniques, and validation methods for using anonymization as an alternative to deletion when retention of aggregate or statistical data serves a legitimate purpose.
Legal Foundation
GDPR Recital 26 — Anonymized Data Outside GDPR Scope
"The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. This Regulation does not therefore concern the processing of such anonymous information, including for statistical or research purposes."
The critical test: whether the data subject is identifiable, taking into account "all the means reasonably likely to be used" either by the controller or "any other person" to identify the natural person.
Article 29 Working Party Opinion 05/2014 on Anonymization Techniques (WP216)
Adopted 10 April 2014, this Opinion establishes that effective anonymization must prevent:
- Singling out: Isolating some or all records which identify an individual in the dataset.
- Linkability: Linking at least two records concerning the same data subject (within the same dataset or between two separate datasets).
- Inference: Deducing, with significant probability, the value of an attribute from the values of a set of other attributes.
The Opinion evaluates randomization and generalization techniques against these three risks.
ICO Anonymization Code of Practice (Updated Guidance)
The ICO provides guidance on anonymization, emphasizing the "motivated intruder" test: could a reasonably competent person with access to resources such as the internet, public libraries, and public records re-identify individuals in the dataset?
Anonymization vs. Pseudonymization
| Characteristic | Anonymization | Pseudonymization |
|---|---|---|
| GDPR status | Outside GDPR scope (Recital 26) | Still personal data (Art. 4(5)) |
| Reversibility | Irreversible — no means to re-identify | Reversible — additional information can re-identify |
| Retention | Can be retained indefinitely | Subject to retention schedule |
| Data subject rights | Do not apply | Fully apply |
| Legal basis | Not required | Required |
| Risk of failure | If anonymization is broken, data reverts to personal data status retroactively | N/A — always personal data |
| Technique | Randomization, generalization, suppression, data masking (irreversible) | Tokenization, encryption, key-based substitution (reversible) |
WP29 Anonymization Techniques
Technique 1: Randomization
Randomization alters the truthfulness of data to break the link between the data and the individual:
| Method | Description | Singling Out | Linkability | Inference | Suitability |
|---|---|---|---|---|---|
| Noise addition | Add random noise to numerical values (e.g., age ± 3 years, salary ± 5%) | Partially mitigates | Partially mitigates | Mitigates | Statistical analysis where exact values are not critical |
| Permutation | Shuffle attribute values within a dataset so that values are no longer linked to the correct record | Partially mitigates | Mitigates | Partially mitigates | Datasets where attribute distributions must be preserved but linkages broken |
| Differential privacy | Add calibrated noise to query results ensuring that the inclusion/exclusion of any single record does not significantly change the output | Mitigates | Mitigates | Mitigates | Aggregate analytics, machine learning training data |
Technique 2: Generalization
Generalization reduces the granularity of data to prevent identification:
| Method | Description | Singling Out | Linkability | Inference | Suitability |
|---|---|---|---|---|---|
| Aggregation | Replace individual values with aggregate statistics (mean, median, count) | Mitigates | Mitigates | Partially mitigates | Reporting, trend analysis |
| K-anonymity | Ensure that each combination of quasi-identifiers appears in at least k records | Mitigates (if k sufficiently large) | Partially mitigates | Does not mitigate | Releasing microdata for research |
| L-diversity | Ensure that within each equivalence class (k-anonymity group), there are at least l distinct values for sensitive attributes | Mitigates | Partially mitigates | Partially mitigates | Datasets with sensitive attributes |
| T-closeness | Ensure that the distribution of sensitive attributes within each equivalence class is close to the overall distribution (distance ≤ t) | Mitigates | Partially mitigates | Mitigates | Datasets where attribute distribution is sensitive |
| Top/bottom coding | Replace extreme values with threshold values (e.g., age >90 becomes "90+") | Partially mitigates | Partially mitigates | Partially mitigates | Protecting outliers in numerical data |
| Data masking | Irreversibly replace characters (e.g., postcode "SW1A 1AA" → "SW1A ***") | Mitigates (for masked fields) | Partially mitigates | Partially mitigates | Reducing granularity of quasi-identifiers |
Show full SKILL.md (519 more words)Show less
Technique 3: Suppression
| Method | Description | Effectiveness |
|---|---|---|
| Record suppression | Remove entire records that are unique or quasi-unique | Eliminates singling out for suppressed records |
| Attribute suppression | Remove entire columns that serve as identifiers or quasi-identifiers | Eliminates linkability via suppressed attributes |
| Cell suppression | Replace specific cell values with null where those values contribute to re-identification risk | Targeted mitigation of singling out |
Anonymization Assessment Workflow
Step 1: Determine Whether Anonymization is Appropriate
[Data Approaching Retention Expiry]
│
▼
[Is there a legitimate purpose for retaining the data in anonymized form?]
│
├── Statistical analysis / reporting ──► Proceed
├── Research purposes (Art. 89) ──► Proceed
├── Training ML models ──► Proceed (with separate legal basis assessment)
├── Historical archiving ──► Proceed
├── No legitimate purpose ──► DELETE (do not anonymize for no reason)
│
▼
[Can the purpose be achieved with anonymized data?]
│
├── Yes ──► Proceed to anonymization
└── No ──► Consider pseudonymization with extended retention (separate assessment)Step 2: Select Anonymization Technique
Based on the data type and intended use:
| Data Type | Recommended Primary Technique | Secondary Technique | Validation Method |
|---|---|---|---|
| Structured numerical (age, salary, amounts) | Generalization (aggregation) + noise addition | Top/bottom coding for outliers | K-anonymity (k ≥ 5) |
| Structured categorical (gender, region, job title) | Generalization (hierarchy-based) + suppression of rare values | Permutation | L-diversity (l ≥ 3) |
| Free text (support tickets, notes) | Full suppression of personal identifiers + generalization of quasi-identifiers | Named entity removal + text generalization | Manual review sample + automated NER validation |
| Transactional (purchase history, usage logs) | Aggregation to cohort level + noise addition | Temporal generalization (day → week → month) | T-closeness (t ≤ 0.15) + k-anonymity (k ≥ 10) |
| Location data | Spatial generalization (precise coordinates → region/city) | Cloaking (minimum area containing k individuals) | K-anonymity (k ≥ 20 for location) |
Step 3: Implement Anonymization
For Orion Data Vault Corp, the standard anonymization pipeline is:
[Source Data — Personal Data Under Retention]
│
▼
[Step 1: Identifier Removal]
- Remove all direct identifiers: name, email, phone, address, NI number,
account number, IP address, device ID
- Remove any unique IDs that could be cross-referenced with other datasets
│
▼
[Step 2: Quasi-Identifier Generalization]
- Age: Generalize to 10-year bands (18-27, 28-37, 38-47, ...)
- Postcode: Truncate to outward code only (SW1A 1AA → SW1A)
- Date of birth: Remove — use age band only
- Job title: Generalize to job function category
- Transaction date: Generalize to month-year
│
▼
[Step 3: Apply K-Anonymity]
- Ensure each combination of quasi-identifiers appears in ≥ k records
- Target: k ≥ 5 for standard data; k ≥ 10 for sensitive data
- Suppress records that cannot achieve k threshold
│
▼
[Step 4: Apply L-Diversity (if sensitive attributes present)]
- Within each equivalence class, ensure ≥ l distinct values for
each sensitive attribute
- Target: l ≥ 3
│
▼
[Step 5: Add Noise (for numerical attributes)]
- Apply calibrated noise (Laplace mechanism for differential privacy)
- Epsilon (ε) parameter: ε ≤ 1.0 for standard data; ε ≤ 0.1 for sensitive data
│
▼
[Step 6: Validation]
- Run re-identification risk assessment (see below)
- If risk > threshold: iterate with stronger parameters
- If risk ≤ threshold: approve anonymized dataset
│
▼
[Anonymized Dataset — Outside GDPR Scope]
- Delete source personal data per retention schedule
- Retain anonymized dataset without retention period constraint
- Document anonymization process in anonymization registerStep 4: Validate Anonymization Effectiveness
Re-Identification Risk Assessment
| Test | Method | Threshold | Action if Failed |
|---|---|---|---|
| Singling out | Attempt to identify unique records using all available quasi-identifiers | < 0.05 (5%) probability of singling out any individual | Increase k-anonymity parameter; suppress unique records |
| Linkability | Cross-reference anonymized dataset with available external datasets (e.g., public records, social media) | No successful linkage in test sample (n ≥ 100) | Remove additional quasi-identifiers; increase generalization |
| Inference | Attempt to infer sensitive attribute values from quasi-identifiers within equivalence classes | No attribute can be inferred with > 80% confidence | Increase l-diversity; apply t-closeness |
| Motivated intruder test (ICO) | Simulate an attack by a motivated individual with access to public resources | Intruder cannot identify any individual with reasonable effort | Strengthen technique parameters; consider full suppression |
K-Anonymity Validation Criteria
| Dataset Size | Minimum k | Rationale |
|---|---|---|
| < 1,000 records | k ≥ 10 | Small datasets are more vulnerable to singling out |
| 1,000 — 100,000 records | k ≥ 5 | Standard protection level |
| > 100,000 records | k ≥ 3 (minimum); k ≥ 5 (recommended) | Larger datasets provide inherent protection |
| Special category data (any size) | k ≥ 10 | Elevated risk from re-identification of sensitive data |
| Location data (any size) | k ≥ 20 | Location data is highly re-identifiable (Montjoye et al., 2013: 4 spatiotemporal points sufficient to uniquely identify 95% of individuals) |
Anonymization Register
Orion Data Vault Corp maintains a register of all anonymization operations:
ANONYMIZATION REGISTER — Orion Data Vault Corp
(Extract as of 2026-03-14)
┌──────────────┬─────────────────┬────────────┬───────────────┬──────────┬──────────────────┬─────────────┐
│ Anon Ref │ Source Category │ Records │ Technique │ k-value │ Re-ID Risk │ Date │
├──────────────┼─────────────────┼────────────┼───────────────┼──────────┼──────────────────┼─────────────┤
│ ANON-2025-041│ CAT-005 Web │ 2.3M │ Aggregation + │ k=15 │ < 0.01% │ 2025-12-01 │
│ │ Analytics │ │ noise addition│ │ │ │
├──────────────┼─────────────────┼────────────┼───────────────┼──────────┼──────────────────┼─────────────┤
│ ANON-2026-008│ CAT-003 Trans. │ 450K │ Generalization│ k=8 │ < 0.02% │ 2026-02-15 │
│ │ Records │ │ + l-diversity │ l=4 │ │ │
├──────────────┼─────────────────┼────────────┼───────────────┼──────────┼──────────────────┼─────────────┤
│ ANON-2026-012│ CAT-009 Support │ 28K │ Text redaction│ k=5 │ < 0.05% │ 2026-03-01 │
│ │ Records │ │ + generalizn. │ │ (manual review │ │
│ │ │ │ │ │ validated) │ │
└──────────────┴─────────────────┴────────────┴───────────────┴──────────┴──────────────────┴─────────────┘Ongoing Monitoring
Annual Re-Identification Risk Review
Anonymized datasets must be reviewed annually because:
- New data sources: Newly available external datasets may enable linkage attacks that were not possible when anonymization was performed.
- Technology advances: Improved computation and AI capabilities may reduce the effectiveness of anonymization techniques over time.
- Contextual changes: Changes in the population represented by the data may make previously anonymous records identifiable.
If a review determines that anonymization is no longer effective, the organization must either:
- Apply additional anonymization techniques to restore effectiveness, or
- Treat the dataset as personal data and apply full GDPR compliance (legal basis, retention schedule, data subject rights).
© 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/anonymization-alternative 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
Anonymization Alternative 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 |
|---|---|---|---|---|---|---|
| Anonymization Alternative this skillmukul975/Privacy-Data-Protection-Skills | 295 | — | ~3.8k | 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 | 586 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 942 | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 942 | 1 repos | ~2.3k | Automated safety check: Pass | MIT |
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Implements age-gating mechanisms for online services to restrict access based on user age.
AI Data Retention
mukul975/Privacy-Data-Protection-Skills
Manages AI model retention and machine unlearning requirements.
Dpia Mitigation Plan
mukul975/Privacy-Data-Protection-Skills
Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d).
Gdpr Accountability
mukul975/Privacy-Data-Protection-Skills
Guides implementation of the GDPR accountability principle under Articles 5(2) and 24, including documentation requirements for policies, DPIAs, RoPA, training records, and breach logs.
Pia Threshold Screening
mukul975/Privacy-Data-Protection-Skills
Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35.
Retention Schedule
mukul975/Privacy-Data-Protection-Skills
Designs and implements data retention schedules compliant with GDPR Article 5(1)(e) storage limitation principle.
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Questions about Anonymization Alternative
What does Anonymization Alternative do?
Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Anonymization Alternative is an agent skill from mukul975/Privacy-Data-Protection-Skills. Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization.
When should I use Anonymization Alternative?
Anonymization Alternative fits situations like: tasks that involve Privacy and GDPR.
How do I install Anonymization Alternative in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill anonymization-alternative -a claude-code`. Or copy the skill folder (skills/privacy/anonymization-alternative in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/anonymization-alternative in your project. Claude Code loads it when a task matches its description.
How do I install Anonymization Alternative in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill anonymization-alternative -a codex`. Or copy the skill folder (skills/privacy/anonymization-alternative in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/anonymization-alternative in your project. Codex loads it when a task matches its description.
Can I use Anonymization Alternative 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 anonymization-alternative -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/anonymization-alternative, .gemini/skills/anonymization-alternative, .github/skills/anonymization-alternative and .opencode/skills/anonymization-alternative in your project.
What does Anonymization Alternative need to run?
Going by SKILL.md and its folder, Anonymization Alternative needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Anonymization Alternative 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 Anonymization Alternative 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 Anonymization Alternative use?
Anonymization Alternative 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 Anonymization Alternative use?
About 3.8k tokens (SKILL.md is roughly 15k 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 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Anonymization Alternative?
Skills that share tags, products or a category with Anonymization Alternative: C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Korean Privacy Terms (kimlawtech/korean-privacy-terms, 586 stars) and Gdpr Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 942 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Anonymization Alternative?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 295 GitHub stars. The repository holds 278 skills in this directory. The repository was last updated on March 16, 2026.
Source: mukul975/Privacy-Data-Protection-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.