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]

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 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]

AI software architecture design — modules, layers, boundaries, design patterns, ADRs, quality attributes, and technical debt strategy for AI-enabled systems. This skill should be used when the user asks to 'design AI system structure', 'define AI module boundaries', 'select AI architecture patterns', 'document AI architecture decisions', 'evaluate AI code architecture', or mentions AI pipelines, feature stores, model serving, drift detection, ML quality attributes, explainability architecture, or AI technical debt. [EXPLICIT]

AI pipeline architecture design — development pipelines, production pipelines, data stores, model registry, CI/CD for AI, and non-functional requirements. This skill should be used when the user asks to 'design AI pipelines', 'architect ML pipelines', 'select data stores for AI', 'design model registry', 'implement CI/CD for ML', 'define AI pipeline requirements', or mentions MLOps, training pipeline, inference pipeline, feature pipeline, Blue and Gold deployment, or pipeline patterns. [EXPLICIT]

AI-specific design patterns and system tactics — Feature Store, Champion-Challenger, Shadow Deployment, Drift Detection, Explainability Wrapper, Canary Deployment, Bulkhead, and traditional patterns adapted for AI. This skill should be used when the user asks to 'select AI design patterns', 'apply ML patterns', 'design drift detection', 'implement feature store', 'plan shadow deployment', 'design champion-challenger', 'select availability tactics for AI', or mentions AI anti-patterns, maintainability tactics, fault recovery for models, or pattern selection for ML systems. [EXPLICIT]

Concept of Operations (CONOPS) for AI systems — system vision, stakeholder mapping, AI-human interaction spectrum, business value assessment, success metrics, and operational modes. This skill should be used when the user asks to 'define the AI operational concept', 'map AI stakeholders', 'design AI-human interaction levels', 'assess AI business value', 'define AI success metrics', 'plan AI operational modes', or mentions CONOPS, IEEE 1362, AI autonomy levels, AI value matrix, or AI system vision. [EXPLICIT]

Guides implementation of AI system architectures — technology selection, pipeline implementation, model serving setup, monitoring deployment, and CI/CD automation. This skill should be used when the user asks to 'implement AI architecture', 'build ML pipeline', 'set up model serving', 'deploy AI system', 'implement MLOps', 'configure drift monitoring', 'set up feature store', or mentions AI implementation plan, ML infrastructure setup, model deployment guide, RAG implementation, or agent framework setup. [EXPLICIT]

Audits existing AI system architectures against best practices — structural integrity, AI quality attributes, pattern adherence, anti-pattern detection, security compliance, and technical debt inventory. This skill should be used when the user asks to 'audit AI architecture', 'review ML system quality', 'assess AI technical debt', 'evaluate AI compliance', 'detect AI anti-patterns', 'review AI security posture', or mentions AI architecture review, AI system assessment, AI quality audit, drift monitoring audit, or AI governance review. [EXPLICIT]

This skill should be used when the user asks 'how do I parallelize this', 'which parallel primitive should I use', 'subagent vs agent teams vs workflow', 'should I use a worktree', 'run work in the background', or needs to choose between subagents, agent view, agent teams, dynamic workflows, and git worktrees. Routes to the right primitive skill; it does not re-explain each one.

This skill should be used when the user asks to 'operate Claude Code agent view', 'use claude agents', 'manage background Claude Code sessions', 'dispatch multiple Claude Code agents', 'monitor background sessions', 'peek reply attach sessions', 'troubleshoot agent view', or compare agent view with subagents, agent teams, dynamic workflows, and worktrees.

This skill should be used when the user asks to 'run Claude Code agent teams', 'orchestrate teammates', 'enable CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS', 'compare agent teams with subagents', 'assign work to Claude teammates', or 'design team lead and teammate coordination'.

Create deterministic Claude Code custom subagent definitions with supported frontmatter, description-driven delegation, bounded system prompts, least-privilege tools, permission and MCP policy, model selection, and validation checks. Use when the user asks to create an agent, add a subagent, make a custom agent, define an agent definition, or build an autonomous subprocess for a specific project responsibility. [EXPLICIT]

Use when the user asks to create an agent constitution, define persistent agent identity, write agent.md, generate an agent governance spec, or add a constitution-grade agent to a multi-agent ecosystem. Produces deterministic agent.md constitutions with 22 required fields covering identity, authority, governance, quality, validation, and version control.

WCAG 2.1/2.2 compliance assessment — a11y testing strategy, remediation priorities, inclusive design. Use when the user asks to 'audit accessibility', 'assess WCAG compliance', 'evaluate a11y', 'review inclusive design', or mentions screen readers, ARIA, color contrast, keyboard navigation. [EXPLICIT]

Seleccion de built-in tools con estrategia Grep then Read then Edit, failure modes y sin Read masivo upfront.

Investigacion adaptativa: mapeo barato, budget de exploracion acotado y re-plan disciplinado solo al invalidar la hipotesis.