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Skills IA lifestyle architecture patterns

Découvrez les skills lifestyle architecture patterns, comparez leurs dépôts et consultez leurs instructions.

Catégories: lifestyle architecture patterns

2,473–2,496 sur 6,879

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GitHub

mcp-architecture

jpoutrin

MCP (Model Context Protocol) architecture patterns, security best practices, and memory management strategies. Use when designing, implementing, or debugging MCP servers in Python (FastMCP) or TypeScript.

GitHub

dbt

jpoutrin

dbt (data build tool) patterns for data transformation and analytics engineering. Use when building data models, implementing data quality tests, or managing data transformation pipelines.

GitHub

sqlmesh

jpoutrin

SQLMesh patterns for data transformation with column-level lineage and virtual environments. Use when building data pipelines that need advanced features like automatic DAG inference and efficient incremental p...

GitHub

pattern-detection

jpoutrin

Identify reusable patterns, best practices, and workflow automations during implementation that could become Product Forge skills, commands, or templates. Use when implementing repetitive structures, applying c...

GitHub

design-system

jpoutrin

Design system management for building and reusing UI components, tokens, and patterns. Use when working with component libraries, design tokens, style guides, or reusable UI patterns to ensure consistency and p...

GitHub

rag-cag-security

jpoutrin

Security patterns for RAG and CAG systems with multi-tenant isolation. Use when building retrieval-augmented or cache-augmented generation systems that require tenant isolation, access control, and secure data...

GitHub

parallel-decompose

jpoutrin

Decompose PRDs and Tech Specs into parallel-executable tasks with contracts, prompts, and dependency graphs. Use when breaking down a PRD for multi-agent execution.

GitHub

sentry-monitoring

oimiragieo

Sentry error tracking and performance monitoring for real-time visibility into application errors, performance issues, and release health

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.

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