Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-data-retentiongit clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-data-retention)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-data-retention"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-data-retention/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-data-retention"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-data-retention.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01614 |
| Opus 5 | $0.00034 | $0.00807 |
| Sonnet 5 | $0.00013 | $0.00323 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
ai-data-retention scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 encoding
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 162 lines · 67 tokens per session scan A 9d943d2a0731
ai-data-retention is a skill published in the GitHub repository mukul975/Privacy-Data-Protection-Skills (272 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,614 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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