416 skills · sorted by relevance
Validates Conductor project artifacts for completeness, consistency, and correctness. Use after setup, when diagnosing issues, or before implementation to verify project context.
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Audit and optimize headings across AEM Edge Delivery Services pages for search intent, hierarchy, and consistency. Extracts all headings (H1-H6), evaluates uniqueness, keyword alignment, specificity, and structural correctness, then generates optimized heading suggestions per page. Use when improving on-page SEO, fixing heading hierarchy issues, or standardizing heading patterns across a site.
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[BETA] Detect, review, and fix code-quality and correctness issues in an AEM as a Cloud Service project — locally, with no external services or network calls. Use whenever a user wants to check, review, assess, audit, scan, modernize, upgrade, or fix AEM Java, Sling Models, OSGi, or Maven code — for example: "check my Sling Models are implemented correctly", "review my @Inject usage", "are my Maven dependencies up to date", "scan this AEM project for issues", "modernize my Sling Models", or "fix code-quality problems". Name the files to assess, or ask it to scan the repo; it detects issues, plans, and — only when you ask — applies surgical edits on a branch or in place, then verifies with mvn compile. It recognises the intent and handles each issue type itself, reporting anything it cannot yet fix. This skill is in beta. Verify all outputs before applying them to production projects.
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Review copy, content, and documentation for clarity, consistency, and correctness.
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Review code changes for correctness, consistency, performance, accessibility, and adherence to repo conventions.
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Conduct one bounded evidence-grounded code review when the user explicitly requests review or ROSE names a concrete unresolved correctness/quality risk; do not trigger automatically after implementation, before every merge, for every bug fix/refactor, or as a completion gate.
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Defines caching strategies with cache keys, TTL values, invalidation triggers, consistency patterns, and correctness checklist. Provides code examples for Redis, CDN, and application-level caching. Use when implementing "caching", "performance optimization", "cache strategy", or "Redis caching".
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Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.
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