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Manages AI model retention and machine unlearning requirements.
Install AI Data Retention
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-data-retention -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-data-retention --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-data-retention .claude/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .claude/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retentionType 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-data-retention -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-data-retention --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-data-retention .agents/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .agents/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retention -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-data-retention --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-data-retention .cursor/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .cursor/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retention--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-data-retention -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-data-retention --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-data-retention .gemini/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .gemini/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retentionInstalls 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-data-retention -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-data-retention .github/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .github/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retention -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-data-retention --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-data-retention .opencode/skills/ai-data-retention && 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-data-retention" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-data-retention into .opencode/skills/ai-data-retention/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-data-retention", 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-data-retention- GitHub stars
- 297
- Token cost
- ~1.9k tokens
- SKILL.md length
- 756 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Manages AI model retention and machine unlearning requirements.
- Works in 4 steps: Membership inference test: Run MI attack… → Output comparison: Compare model outputs… → Canary test: If canary records were… → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, AI Data Retention Categories, Retention Policy Framework and Machine Unlearning Techniques, plus 3 more sections
- Runs Python scripts from its folder
What it does
AI Data Retention is an agent skill from mukul975/Privacy-Data-Protection-Skills. Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
Its SKILL.md is about 1.9k 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. 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 ai-data-retention skill to manage AI model retention and machine unlearning requirements”
- “/ai-data-retention”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Membership inference test: Run MI attack on unlearned records — should classify as non-members
- Output comparison: Compare model outputs with and without the unlearned data
- Canary test: If canary records were included, verify they are no longer extractable
- Statistical test: Compare model to one retrained from scratch on the same data minus deleted records
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 Data Retention loads about 1.9k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 756 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). 756 words, ~1,920 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-data-retention/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-data-retention
- description
- Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- ai-retention, machine-unlearning, model-versioning, training-data-deletion, retraining, storage-limitation
AI Model Retention and Unlearning
Overview
GDPR Art. 5(1)(e) storage limitation requires that personal data be kept no longer than necessary for the processing purpose. For AI systems, this creates complex retention challenges: training data used to build a model may no longer be needed once training is complete, but the model itself encodes information about the training data. Machine unlearning — the process of removing the influence of specific data from a trained model — is an emerging field that addresses the gap between deleting training data and eliminating its influence from model parameters. This skill provides retention policies, deletion verification methods, and machine unlearning techniques for AI compliance.
AI Data Retention Categories
| Data Category | Description | Retention Consideration |
|---|---|---|
| Raw training data | Original personal data used for model training | Delete after training unless retraining justifies retention |
| Processed training data | Cleaned, augmented, feature-engineered data | Same as raw — delete when training purpose exhausted |
| Validation/test data | Data used for model evaluation | Retain for model audit and comparison; pseudonymise |
| Model weights/parameters | Trained model artefacts encoding training data information | Retain while model is deployed; delete on decommission |
| Inference logs | Inputs and outputs of model predictions | Retention based on purpose (audit, debugging, rights exercise) |
| Model metadata | Training configuration, hyperparameters, provenance | Retain for compliance documentation; low privacy risk |
| Embedding vectors | Dense representations derived from personal data | May contain personal data — apply retention policy |
Retention Policy Framework
Training Data Retention Decision Tree
Training data category identified
│
├─ Is the data still needed for model retraining?
│ ├─ YES → Retain with documented justification and review date
│ └─ NO → Continue
│
├─ Is the data needed for model validation or audit?
│ ├─ YES → Retain in pseudonymised form with access controls
│ └─ NO → Continue
│
├─ Is the data needed for data subject rights exercise?
│ ├─ YES → Retain for rights exercise period, then delete
│ └─ NO → Continue
│
├─ Is there a legal obligation to retain?
│ ├─ YES → Retain per legal requirement
│ └─ NO → DELETE the training data
│
└─ After deletion: assess model for residual data encodingModel Lifecycle Retention
| Phase | Retention Rule |
|---|---|
| Development | Training data retained during active development |
| Deployment | Training data deleted unless retraining is planned within defined period |
| Operation | Inference logs retained per purpose (30 days debug, 1 year audit) |
| Retraining | New training data collected; old data deleted post-training |
| Decommission | All model artefacts, training data, and logs deleted; retain only compliance documentation |
Machine Unlearning Techniques
Exact Unlearning
Full retraining: Retrain the model from scratch on the dataset minus deleted records.
| Property | Value |
|---|---|
| Guarantee | Complete — model has no knowledge of deleted data |
| Cost | Very high — full training cost for each deletion request |
| Feasibility | Impractical for large models or frequent deletion requests |
| When to use | Small models, infrequent requests, high-sensitivity data |
SISA (Sharded, Isolated, Sliced, Aggregated) Training
Train model on sharded data partitions. To unlearn, retrain only the affected shard.
| Property | Value |
|---|---|
| Guarantee | Exact within the affected shard |
| Cost | 1/k of full retraining (k = number of shards) |
| Feasibility | Requires SISA architecture from the start |
| Trade-off | Model accuracy may decrease with fewer shards contributing |
Approximate Unlearning
Gradient-Based Unlearning
Apply gradient ascent on the data to be forgotten, then fine-tune on remaining data.
| Property | Value |
|---|---|
| Guarantee | Approximate — statistically similar to retrained model |
| Cost | Low — few gradient steps |
| Feasibility | Works for most differentiable models |
| Verification | Requires membership inference testing to verify |
Show full SKILL.md (312 more words)Show less
Influence Function-Based Unlearning
Use influence functions to estimate the effect of removing data and adjust model accordingly.
| Property | Value |
|---|---|
| Guarantee | Approximate — first-order approximation |
| Cost | Medium — requires Hessian computation |
| Feasibility | Best for smaller models or linear models |
Unlearning Verification
After applying unlearning, verify effectiveness:
- Membership inference test: Run MI attack on unlearned records — should classify as non-members
- Output comparison: Compare model outputs with and without the unlearned data
- Canary test: If canary records were included, verify they are no longer extractable
- Statistical test: Compare model to one retrained from scratch on the same data minus deleted records
Model Versioning for Compliance
Version Control Requirements
| Element | Documentation |
|---|---|
| Model version ID | Unique identifier (e.g., model-v2.3.1-20260314) |
| Training data snapshot | Hash of training dataset used for this version |
| Training date | When training was executed |
| Data deletions applied | Which data subject deletions are reflected in this version |
| Unlearning applied | Any approximate unlearning applied since last full retraining |
| Privacy properties | DP epsilon, MI test results for this version |
| Deployment dates | When deployed and when retired |
Retraining Triggers
| Trigger | Action |
|---|---|
| Accumulated deletion requests exceed threshold | Full retraining on updated dataset |
| Scheduled periodic retraining | Incorporate all pending deletions |
| Privacy audit reveals unacceptable leakage | Retrain with enhanced privacy measures |
| Model performance degradation | Retrain with current data (post-deletions) |
| Regulatory change | Assess if retraining needed for compliance |
Enforcement Relevance
- EDPB Guidelines 04/2025: Training data retention must be justified; deletion of training data does not automatically eliminate GDPR obligations for the model if it encodes personal data.
- Garante v. OpenAI (2023): Required mechanism for data deletion from training data; acknowledged technical challenges but expected good faith effort.
- EDPB ChatGPT Taskforce (2024): Controllers must demonstrate capability to address erasure requests affecting training data.
Integration Points
- ai-data-subject-rights: Erasure rights implementation requires unlearning
- ai-dpia: Retention and deletion capability assessed in DPIA
- ai-model-privacy-audit: Audit verifies deletion effectiveness
- ai-training-lawfulness: Retention justification part of lawful basis 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-data-retention 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 Data Retention 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 Data Retention this skillmukul975/Privacy-Data-Protection-Skills | 297 | — | ~1.9k | 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 | 943 | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 943 | 1 repos | ~2.3k | Automated safety check: Pass | MIT |
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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.
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Related
Categories
Questions about AI Data Retention
What does AI Data Retention do?
Manages AI model retention and machine unlearning requirements. AI Data Retention is an agent skill from mukul975/Privacy-Data-Protection-Skills. Manages AI model retention and machine unlearning requirements.
When should I use AI Data Retention?
AI Data Retention fits situations like: tasks that involve Privacy and GDPR.
How do I install AI Data Retention in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-data-retention -a claude-code`. Or copy the skill folder (skills/privacy/ai-data-retention in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-data-retention in your project. Claude Code loads it when a task matches its description.
How do I install AI Data Retention in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-data-retention -a codex`. Or copy the skill folder (skills/privacy/ai-data-retention in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-data-retention in your project. Codex loads it when a task matches its description.
Can I use AI Data Retention 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-data-retention -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-data-retention, .gemini/skills/ai-data-retention, .github/skills/ai-data-retention and .opencode/skills/ai-data-retention in your project.
What does AI Data Retention need to run?
Going by SKILL.md and its folder, AI Data Retention needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Data Retention 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 Data Retention 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 Data Retention use?
AI Data Retention 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 Data Retention use?
About 1.9k tokens (SKILL.md is roughly 7.7k 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 763 tokens, read only when the agent opens those files.
What are the alternatives to AI Data Retention?
Skills that share tags, products or a category with AI Data Retention: 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, 943 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Data Retention?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 297 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.