mao-discovery-framework

Use when the user asks to "assess waterfall maturity", "evaluate traditional PM practices", "check PMBOK adherence", "review predictive methodology readiness", "audit phase-gate compliance", or mentions waterfall assessment, traditional PM maturity, PMBOK compliance, PRINCE2 maturity, predictive PM evaluation, earned value adoption.

Use when the user asks to "implement waterfall", "plan PMBOK phases", "set up PRINCE2", "define stage gates", "design predictive lifecycle", "configure change control", or mentions waterfall, traditional PM, predictive lifecycle, stage-gate, PMBOK, PRINCE2, earned value management.

CLI interactivo de inicialización que configura el entorno del cliente, pre-puebla discovery/, ejecuta G0 security scan y prepara el contexto para discovery.

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.

Adoption strategy design producing communication plan, training roadmap, resistance management tactics, and reinforcement mechanisms. Use when the user asks to "design adoption strategy", "plan change adoption", "communication plan", "training needs analysis", "resistance management", "adoption roadmap", "change communication", or mentions "post-implementation adoption", "user onboarding strategy", "technology adoption plan".

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.

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.

AI Center services discovery — AI readiness assessment using MetodologIA AI SCALE methodology, use case portfolio prioritization, data readiness evaluation, model inventory, AI governance assessment, infrastructure evaluation, MetodologIA AI product integration, and AI adoption roadmap. Use when the user asks to "assess AI readiness", "evaluate AI maturity", "AI discovery", "AI use case prioritization", "MLOps assessment", "AI governance evaluation", "AI adoption roadmap", "AI strategy assessment", "evaluate AI infrastructure", "AI product fit", or mentions "AI SCALE", "responsible AI", "AI pilots", "ML pipeline", "AI Center of Excellence", "LLM adoption", "generative AI strategy".

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.

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.

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.

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.

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.

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.

API design & governance — REST/GraphQL/gRPC, versioning, rate limiting, DX, contract-first. Use when the user asks to "design an API", "define API strategy", "implement contract-first", "set up API governance", "design API versioning", "improve developer experience", or mentions REST, GraphQL, gRPC, AsyncAPI, OpenAPI, API gateway, rate limiting, or API catalog.

Target state (TO-BE) architecture design — C4 L2 containers, ADRs, nightmare scenario mitigations, MVP component, phased Strangler Fig migration. Use when the user asks to "design the target architecture", "create a TO-BE architecture", "plan a migration strategy", "define ADRs for a new system", "mitigate nightmare scenarios", or mentions Strangler Fig, C4 diagrams, saga pattern, anti-corruption layer, or legacy modernization.

Universal current-state assessment producing 10-section analysis for ANY MetodologIA service type. Use when the user asks to "analyze the codebase", "assess current architecture", "run AS-IS analysis", "technical audit", "evaluate tech debt", "code quality assessment", "assess current state", "service assessment", "QA maturity", "PMO assessment", "RPA readiness", "data maturity", "cloud readiness", "design maturity", "talent gap analysis", or mentions "Phase 1", "current state", "legacy system review", "technical health check".

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.

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.

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.