CI/CD and release engineering: pipeline architecture, multi-environment strategy, deployment rollback (blue-green/canary), code-quality gates, dependency governance, git-hook automation. [EXPLICIT] Trigger: 'ci/cd', 'pipeline', 'rollback', 'canary', 'blue-green', 'release', 'quality gate', 'staging'.

Designs frontend module boundaries, interface contracts, and composition patterns for scalable component hierarchies. Covers smart/dumb patterns, compound components, and dependency injection. [EXPLICIT] Trigger: 'component architecture', 'module boundary', 'composition pattern', 'interface contract'

Router de extensiones Claude Code para decidir y componer command/skill, scope, context fork y controles de invocacion. Deriva la autoria a claude-command-authoring o claude-skill-authoring y permisos a tool-permission-policy; no cubre Claude Platform.

Data pipeline architecture — ingestion, orchestration, quality, lineage, SLAs. Use when the user asks to 'design data pipelines', 'architect ingestion', 'set up orchestration', 'plan data lake', 'design lakehouse', or mentions Airflow, Dagster, CDC, data lineage, or pipeline SLAs. [EXPLICIT]

This skill should be used when the user asks to design, configure, audit, or choose Claude Code channels, MCP channel servers, --channels startup flags, external push events, channel plugins, permission relay, or channel-vs-schedule/hook/headless decisions.

BI solution design — semantic layers, dashboard patterns, self-service analytics, KPI frameworks. Use when the user asks to 'design BI architecture', 'build a KPI framework', 'set up self-service analytics', 'design dashboard hierarchy', 'create a semantic layer', or mentions metric trees, drill-down patterns, or reporting strategy. [EXPLICIT]

Generate Gherkin BDD scenarios across all quality angles — functional, accessibility, security, performance, SEO, offline, UI, backend, data, DevSecOps, CI/CD. Runner-agnostic step definitions with traceability to requirements and constitutional principles. [EXPLICIT] Trigger: 'BDD', 'Gherkin', 'Given When Then', 'full-spectrum testing', 'acceptance scenarios'

Data privacy governance: privacy-by-design, PII/PHI classification and minimization, encryption, anonymization, retention, access evidence, and tamper-evident audit trails. Routes data quality to data-quality and catalog/lineage strategy to data-strategy.

Guides implementation of AI/GenAI architectures on AWS — Bedrock setup, SageMaker pipelines, OpenSearch vector stores, API Gateway configuration, security hardening, cost controls, and deployment automation. This skill should be used when the user asks to 'implement AI on AWS', 'set up Bedrock', 'deploy SageMaker pipeline', 'configure OpenSearch for RAG', 'implement AWS AI security', 'set up AWS AI monitoring', or mentions AWS AI deployment, Bedrock Knowledge Base setup, SageMaker endpoint deployment, AWS GenAI implementation, or AWS AI CI/CD pipeline. [EXPLICIT]

Data quality framework — profiling, validation, anomaly detection, data contracts, SLA monitoring. Use when the user asks to 'design data quality framework', 'set up data contracts', 'plan data validation', 'detect data anomalies', 'define data SLAs', or mentions data profiling, quarantine patterns, or remediation workflows. [EXPLICIT]

Designs AWS cloud architectures for AI and GenAI workloads applying the Well-Architected Framework GenAI Lens (6 pillars: GENOPS, GENSEC, GENREL, GENPERF, GENCOST, GENSUS), AWS service selection matrices, RAG/Agent/Fine-Tuning patterns, cost optimization strategies, and enterprise reference architectures. Activated when designing, evaluating, or migrating AI systems on AWS. [EXPLICIT]

Audits AWS AI/GenAI architectures against the Well-Architected GenAI Lens — operational excellence, security, reliability, performance, cost optimization, and sustainability. This skill should be used when the user asks to 'audit AWS AI architecture', 'review Bedrock configuration', 'assess SageMaker security', 'optimize AWS AI costs', 'evaluate AWS GenAI compliance', 'review AWS Well-Architected for AI', or mentions AWS AI audit, Bedrock audit, SageMaker review, AWS GenAI security assessment, or AWS AI cost optimization review. [EXPLICIT]

Designs data architecture, governance frameworks, quality standards, and pipeline strategies. Covers data modeling, cataloging, lineage tracking, and analytics enablement. [EXPLICIT] Trigger: 'data architecture', 'data governance', 'data quality', 'data pipeline', 'data strategy'

This skill should be used when the user asks to publish or share Claude Code artifacts, create a visual or interactive session output, decide whether an Artifact is appropriate, validate artifact source and output contracts, handle artifact privacy, produce share links, or choose between workspace artifacts, Claude Code Artifacts, files, and output styles.

Hands-on application security: authn/authz (OAuth/SAML/IAM), RBAC, input validation and sanitization, CORS, HTTP security headers, rate limiting, and security audit/testing (SAST/DAST). [EXPLICIT] Implementation-level, complements security-architecture (strategy). Trigger: 'app security', 'rbac', 'cors', 'rate limit', 'input sanitization', 'security headers', 'xss'.

Designs database schemas, indexing strategies, and data models across the target platforms — relational (MySQL/MariaDB on Hostinger, Postgres/Neon on Vercel) and NoSQL document stores (Firestore on Firebase, MongoDB). [EXPLICIT] Covers normalization, denormalization trade-offs, and query optimization. [EXPLICIT] Trigger: 'database', 'schema', 'data model', 'indexing'

Designs REST and GraphQL API contracts with OpenAPI/Swagger specifications, versioning strategies, pagination patterns, error handling, and rate limiting. [EXPLICIT] Produces production-ready API specifications. [EXPLICIT] Trigger: 'API design', 'REST', 'GraphQL', 'OpenAPI', 'API contract'

Analytics pipeline design — dbt-style transformations, data modeling, testing, documentation. Use when the user asks to 'design analytics models', 'set up dbt project', 'plan data transformations', 'define data contracts', 'model star schema', or mentions staging models, marts, incremental strategies, or materializations. [EXPLICIT]

Comprehensive testing strategy for AI systems — testing scope matrix (6 types x 6 layers), model prediction testing, data quality testing, compliance and fairness testing, integration approaches, and CI/CD test automation. This skill should be used when the user asks to 'define AI testing strategy', 'test ML models', 'design data quality tests', 'plan fairness testing', 'test AI pipelines', 'design integration tests for ML', or mentions adversarial testing, drift simulation, model regression testing, bias testing, explainability testing, or AI test automation. [EXPLICIT]

This skill should be used when the user asks to create Claude Code deep links, build claude-cli://open onboarding links, prefill Claude Code prompts with cwd or repo, document clickable runbook links, audit deep-link safety, troubleshoot Claude Code protocol handlers, or choose between deep links, headless claude -p, scripts, channels, hooks, scheduled tasks, or Remote Control.