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Found 1,499 Skills
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.
Microservices distributed architecture pattern. Use for scalable systems.
Serverless architecture with FaaS and BaaS. Use for cloud functions.
Astro static site builder with islands architecture and content collections. Use for content sites.
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
Create diagrams, charts, and visual assets for security documentation. Generate network diagrams, architecture visuals, and data visualizations. Use when creating visual content for reports or presentations.
TYPO3 extension testing (unit, functional, E2E, architecture, mutation). Use when setting up test infrastructure, writing tests, or configuring CI/CD.
Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.
Example skill demonstrating the Skills-as-Containers pattern with workflows, assets, and natural language routing. This is a teaching tool showing the complete PAI v1.2.0 architecture. USE WHEN user says 'show me an example', 'demonstrate the pattern', 'how do skills work', 'example skill'
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
Generate Python FastAPI code following project design patterns. Use when creating models, schemas, repositories, services, controllers, database migrations, authentication, or tests. Enforces layered architecture, async patterns, OWASP security, and Alembic migration naming conventions (yyyymmdd_HHmm_feature).
End-to-end form handling with react-hook-form, Zod schemas, validation patterns, error messaging, field arrays, and multi-step wizards. Use for complex forms, validation architecture, autosave, field dependencies. Activate on "form validation", "react-hook-form", "Zod", "form error", "multi-step form", "wizard". NOT for simple HTML forms, backend validation only, or non-React frameworks.