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System Design

Checked
jwynia·vlatest·frontend

Designs scalable, resilient system architectures based on requirements, considering trade-offs and potential bottlenecks.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jwynia/agent-skills --skill system-design --full-depth -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jwynia

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned jwynia/agent-skills at e02ec7e226.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e02ec7e226
Manifest
skills/tech/development/architecture/system-design/SKILL.md
Scanned
5 files, 672 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The system-design skill enables AI agents to analyze complex problems and design scalable, efficient systems. It supports tasks such as defining architecture components, selecting appropriate technologies, and ensuring system reliability and performance.

When to use it

  • Designing a backend infrastructure for a new web application
  • Planning the technical architecture of a distributed system
  • Evaluating trade-offs between different system design patterns

Key capabilities

  • Analyzes requirements to determine optimal system structure
  • Recommends technologies based on scalability, security, and performance needs
  • Provides guidance on fault tolerance, data flow, and component interactions

Example prompts

  • "Design a microservices architecture for an e-commerce platform."
  • "What are the best practices for ensuring high availability in cloud systems?"
  • "Propose a system design for real-time chat application with 1 million users."

Tips & gotchas

  • Provide clear and detailed requirements to ensure accurate recommendations.
  • Be aware that complex designs may require iterative refinement based on feedback and constraints.