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 age-verification-methodsgit 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/age-verification-methods)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/age-verification-methods"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/age-verification-methods/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/age-verification-methods"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/age-verification-methods.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 | $0.00076 | $0.03474 |
| Opus 5.5 | $0.00030 | $0.01390 |
| Sonnet 5.5 | $0.00015 | $0.00695 |
| Haiku 4.5 | $0.00008 | $0.00347 |
Grade A, and why
age-verification-methods 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.
This is a copy
97% identical to age-verification-methods — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Age Verification and Estimation Methods
Overview
Age verification and age estimation are distinct but complementary approaches to determining whether a user is a child for the purpose of applying appropriate data protection safeguards. Age verification provides a definitive confirmation of age through documentary or transactional evidence. Age estimation provides a probabilistic assessment of age using technological methods such as facial analysis, behavioural analysis, or device signals. The selection of an appropriate method requires balancing accuracy, privacy impact, accessibility, and proportionality. This skill covers the full spectrum of available methods, their regulatory context under the GDPR, UK AADC, COPPA, and emerging legislation such as the EU Digital Services Act (DSA) and the UK Online Safety Act 2023, and provides implementation guidance based on ICO and CNIL recommendations.
Regulatory Context
GDPR Article 8(2)
"The controller shall make reasonable efforts to verify in such cases that consent is given or authorised by the holder of parental responsibility over the child, taking into consideration available technology."
The "reasonable efforts" standard is context-dependent. The EDPB has not prescribed specific technologies but expects controllers to adopt verification proportionate to the risk of the processing.
UK AADC Standard 3 — Age-Appropriate Application
"Take a risk-based approach to recognising the age of individual users and ensure you effectively apply the standards in this code to child users." The ICO guidance states that the level of certainty required depends on the risks to children from the processing. Higher risks demand more robust age assurance methods.
UK Online Safety Act 2023
Section 11(3) requires providers of regulated user-to-user services and search services to use "proportionate systems or processes" designed to prevent children from encountering primary priority content that is harmful to children. Ofcom's codes of practice specify age verification as a recommended measure for pornographic content and age estimation for broader content categories.
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 · 230 lines · 76 tokens per session scan A ea286b264abd
age-verification-methods is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 3,474 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to age-verification-methods, differing in 20 lines, and is treated as a copy.
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