Audit web targets across 8 security and compliance layers.
npx skills add https://github.com/RideMatch1/a.e.g.i.s --skill aegis-audit
Please help me install this Agent Skill. Skill: aegis-audit Source: https://github.com/RideMatch1/a.e.g.i.s/tree/main/packages/skills/skills/foundation/aegis-native/aegis-audit Command: npx skills add https://github.com/RideMatch1/a.e.g.i.s --skill aegis-audit
8-layer paranoid-audit delivers automated, end-to-end security and compliance checks for web targets, surfacing hidden risks and producing a structured 4-section report ready for remediation.
Run the aegis-audit skill against a target URL or local repo to generate the full 8-layer audit and the canonical 4-section Schadens-Diagnose report.
High-intent search queries and answers about installing and using this skill.
You can automate GDPR compliance and security auditing by running an 8-layer scan against live URLs to check HTTP headers, HTML, Impressum, DSE, and cookie consent. It generates a canonical report with cross-layer correlation and a risk euro-range synthesis to support remediation prioritization.
A comprehensive DSGVO security audit covers eight layers: HTTP headers, HTML structure, Impressum, Datenschutzerklärung, cookie consent, industry-specific checks, code-cross-check, and Schadens-Diagnose. It correlates risks across layers to reduce abmahn-risk and outputs a structured 4-section report.
Yes, you can run a legal compliance audit against local repositories, staging environments, or production sites. The audit applies deterministic layer ordering with no mocked data, validating legal cross-references and generating client-facing deliverables suitable for CI pipelines.
Prioritize website security vulnerabilities by generating a risk euro-range synthesis from the Schadens-Diagnose layer. This evaluates cross-layer correlations and surfaces hidden risks, producing a structured 4-section report that quantifies financial exposure to support remediation decisions.
Cookie consent and Impressum checks work during an automated web audit by applying deterministic layer-order processing to the live target. The audit validates legal cross-references within the HTML structure and correlates findings across the 8 layers to reduce abmahn-risk.
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