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Breakdown Epic Arch

Official
github·vlatest·frontend·MIT

This skill dissects complex "epic" architecture into manageable components for easier understanding and modification.

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 github/awesome-copilot --skill breakdown-epic-arch -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

github

Matched to the vendor's own GitHub organisation.

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 github/awesome-copilot at 6c4d33b9cf.

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

Commit
6c4d33b9cf
Manifest
skills/breakdown-epic-arch/SKILL.md
Scanned
1 file, 67 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 breakdown-epic-arch skill enables AI agents to analyze and deconstruct complex architectural designs or systems, particularly those related to software engineering or system architecture. It provides insights into components, dependencies, and relationships within an epic or large-scale project.

When to use it

  • To understand the structure of a large software system before making modifications.
  • During code reviews to identify potential bottlenecks or inefficiencies in architectural design.
  • For educational purposes when teaching about system architecture and component interactions.
  • When planning the integration of new features into an existing complex system.

Key capabilities

  • Analyzes system components and their relationships.
  • Identifies dependencies within a software architecture.
  • Breaks down large systems into manageable parts for easier understanding.

Example prompts

  • "Breakdown the architecture of this microservices-based application."
  • "Analyze the component structure of the given system design document."
  • "Identify all dependencies in this software project's architecture."

Tips & gotchas

  • Ensure that the input provided includes sufficient detail about the system or architecture for accurate analysis.
  • This skill may not perform well with vague or incomplete descriptions, so clarity is key to getting useful results.