🧩
DevOpsPython

Designing Distributed Systems

by ancoleman

Designing Distributed Systems is a DevOps skill for Claude Code, published by ancoleman in ai-design-components.

516 stars73 forkson ancoleman/ai-design-componentsAdded 2026/08/26+1% in starsRepository updated 2025/12/11
ai-designanthropicclaudeclaude-codeclaude-skillsdesign-systemfull-stackreactskillstypescriptui-components
Install in seconds
Install Designing Distributed Systems
Copy Designing Distributed Systems into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/ancoleman/ai-design-components/tree/main/skills/designing-distributed-systems ~/.claude/skills/designing-distributed-systems

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/ancoleman/ai-design-components.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/designing-distributed-systems/SKILL.md in ancoleman/ai-design-components
Installs to
~/.claude/skills/designing-distributed-systems
Collection
One of 25 skills cataloged from this repository
Category
DevOps798 skills

What Designing Distributed Systems does

Designing Distributed Systems covers CAP/PACELC, consistency models, replication, partitioning, transactions, and resilience patterns for building scalable fault-tolerant architectures. Use it when designing microservices or distributed systems that must balance latency, availability, and correctness.

Designing Distributed Systems is cataloged under DevOps on DirSkills. Designing Distributed Systems comes from a repository tagged ai-design, anthropic, claude, claude-code and claude-skills.

Documentation

README

Designing Distributed Systems

Design scalable, reliable, and fault-tolerant distributed systems using proven patterns and consistency models.

Purpose

Distributed systems are the foundation of modern cloud-native applications. Understanding fundamental trade-offs (CAP theorem, PACELC), consistency models, replication patterns, and resilience strategies is essential for building systems that scale globally while maintaining correctness and availability.

When to Use This Skill

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Designing Distributed Systems

  • What else does ancoleman publish alongside Designing Distributed Systems?

    Designing Distributed Systems is one of 25 skills that DirSkills catalogs from ancoleman/ai-design-components, the repository it ships in. Its siblings there include AI Chat Interface Components, AI Data Engineering and AWS Patterns. Each one is a separate skill with its own page in this directory, installs the same way Designing Distributed Systems does, and is maintained by ancoleman in that same repository. The rest of the collection is listed on the ancoleman/ai-design-components page.

  • How does Designing Distributed Systems compare to other DevOps skills?

    Designing Distributed Systems ranks #703 by stars among the 798 DevOps skills in this catalog. The most-starred ones next to it are Backend Patterns, API Connector Builder and Migration. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Designing Distributed Systems against them. Open each page to compare what they document and how they install.

More from ancoleman/ai-design-components

Designing Distributed Systems is one of 25 skills cataloged on DirSkills from ancoleman/ai-design-components.

See all 25 skills
💬
5d ago

AI Chat Interface Components

AI Chat Interface Components builds chat UIs for assistants, copilots, and chatbots with streaming responses, context handling, multi-modal input, and response controls. Use it when implementing conversational interfaces with feedback, regeneration, and tool call visualization.
AI Engineering
51673
🗄️
5d ago

AI Data Engineering

AI Data Engineering builds data infrastructure for AI and ML systems, including RAG pipelines, embeddings, feature stores, and orchestration. Use it when preparing data for retrieval, semantic search, or production model serving.
Data
51673
☁️
5d ago

AWS Patterns

AWS Patterns provides decision frameworks and implementation patterns for choosing AWS services and designing cloud architectures. Use it when selecting compute, storage, or database services, or applying Well-Architected principles.
DevOps
51673
🧩
5d ago

Assembling Components

Assembling Components turns outputs from AI Design Components skills into production-ready component systems with validated tokens, correct imports, and framework-specific scaffolding. Use it after theming, layout, dashboard, data-viz, or feedback skills to wire React, Next.js, Python, or Rust projects.
Frontend
51673
☁️
5d ago

Azure Patterns

Azure Patterns designs Azure cloud architectures using service selection, security, governance, and cost best practices. Use it when building new Azure applications or migrating workloads to Azure.
DevOps
51673
⚙️
5d ago

Building CI Pipelines

Building CI Pipelines constructs secure, efficient CI/CD workflows for testing, building, and deployment. Use it to choose and optimize pipelines in GitHub Actions, GitLab CI, Argo Workflows, or Jenkins, including SLSA, caching, and monorepo patterns.
DevOps
51673