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Audit crawlability, rendering, metadata, and structured data with evidence-backed fixes. Use for indexing problems, migrations, or pre-launch SEO checks.

Design first-run experiences and onboarding flows for AI agents: capability discovery, permission grants, and initial task routing.

Design AI writing assistant features: tone control, suggestion timing, feedback loops, and human-in-the-loop editing.

Use when reviewing or writing Kubernetes manifests; audits workloads for reliability, security, and cost issues and produces ready-to-apply fixes.

Design feature flag strategies: flag types, rollout percentages, kill switches, technical debt management, and cleanup schedules.

Optimize how context is assembled for LLM calls: system prompt design, few-shot example selection, context pruning, and token budget management.

Design WebSocket and real-time communication systems: connection management, reconnection, message protocols, and scaling.

Design canary and progressive deployment strategies: traffic splitting, health metrics, automatic rollback, and analysis windows.

Audit UIs against WCAG 2.2 AA and deliver prioritized, code-level fixes. Use when asked for an accessibility review, a11y compliance, or AT bug reports.

Design micro-frontend architectures: module federation, route splitting, shared dependencies, and team boundaries.

Scan and harden container images: base image vulnerabilities, runtime permissions, distroless patterns, and admission policies.

Design real-time stream processing: windowing, watermarking, exactly-once semantics, and state management.

Plan and execute LLM fine-tuning: dataset preparation, method selection (LoRA, QLoRA, full), evaluation, and deployment.

Audit codebases for i18n readiness: hardcoded strings, locale formatting, RTL support, pluralization rules, and translation workflow.

Use when a prompt underperforms and needs systematic diagnosis, single-variable experiments, and A/B evidence instead of guess-and-check rewrites.

Design disaster recovery strategies: RTO/RPO targets, backup automation, failover procedures, and DR testing.

Design multi-layer caching strategies: browser, CDN, application, and database caching with invalidation patterns.

Manage Drupal in all popular forms: drush CLI, REST/JSON:API, module management, content types, Views, performance caching, and security hardening.

Audit error handling across a codebase: unhandled rejections, swallowed exceptions, missing retries, and inconsistent error types.

Plan ML model deployment: serving patterns, A/B testing, canary releases, monitoring, and rollback for production ML.

Audit application configuration: environment variables, secrets management, config validation, and environment parity.

Design and implement ML feature stores: online/offline serving, feature freshness, training-serving skew prevention, and feature discovery.

Audit code for race conditions, deadlocks, and thread safety issues in async and multi-threaded code.

Implement tracing, logging, and monitoring for AI agent systems: LLM call traces, tool execution logs, latency tracking, and replay debugging.