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 onfire7777/universal-ai-skills-library --skill auto-deletion-workflowgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/auto-deletion-workflow)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/auto-deletion-workflow"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/auto-deletion-workflow/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/onfire7777/universal-ai-skills-library/auto-deletion-workflow"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/auto-deletion-workflow.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 | $0.00064 | $0.03841 |
| Opus 5.5 | $0.00026 | $0.01536 |
| Sonnet 5.5 | $0.00013 | $0.00768 |
| Haiku 4.5 | $0.00006 | $0.00384 |
Grade A, and why
auto-deletion-workflow 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 1mo 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Data Deletion Workflow
Overview
Automated deletion workflows ensure that personal data is removed from all systems when retention periods expire or when a valid erasure request is received under GDPR Article 17. Manual deletion at scale is error-prone and fails to meet the storage limitation principle consistently. This skill defines the architecture, logic, and operational procedures for building automated deletion pipelines that handle cascading dependencies, maintain referential integrity, produce audit-grade confirmation logs, and satisfy both scheduled retention expiry and on-demand erasure requests.
Legal Foundation
GDPR Article 17(1) — Right to Erasure
The data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay, and the controller shall have the obligation to erase personal data without undue delay where one of the specified grounds applies.
GDPR Article 5(1)(e) — Storage Limitation
Personal data shall be kept for no longer than is necessary for the purposes for which the personal data are processed. Automated deletion is the primary technical measure for enforcing this principle at scale.
GDPR Article 25(1) — Data Protection by Design
The controller shall implement appropriate technical and organisational measures designed to implement data-protection principles, such as data minimisation, in an effective manner. Automated deletion is a core by-design control.
GDPR Article 30(1)(f) — Envisaged Time Limits for Erasure
Records of processing activities must include, where possible, the envisaged time limits for erasure of the different categories of data. Automated deletion systems operationalize these envisaged time limits.
Automated Deletion Architecture
Deletion Trigger Types
| Trigger Type | Source | SLA | Priority |
|---|---|---|---|
| Retention expiry | Retention schedule metadata reaching calculated deletion date | Within 30 days of expiry | Standard |
| Data subject erasure request | Verified Art. 17 request via DSAR workflow | Within 30 calendar days of verified request | High |
| Consent withdrawal | Consent management platform event | Within 30 days (best practice: 72 hours) | High |
| Account closure | Customer account termination event | Per retention schedule (post-closure retention period) | Standard |
| Purpose completion | Processing purpose fulfilled, no further legal basis | Within 30 days of purpose completion | Standard |
| Legal hold release | Litigation hold lifted by Legal counsel | Within 14 days of hold release | Standard |
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.
- 1mo ago First seen · 325 lines · 64 tokens per session scan A 842ad72c5f60
auto-deletion-workflow is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 3,841 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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