C4 Architecture
CheckedC4 Architecture generates clean, semantic HTML and CSS from component descriptions – boosting front-end development speed & consistency.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davila7/claude-code-templates --skill c4-architecture -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
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.
Automated safety checks
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 davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 5 files, 1,775 lines
About this skill
C4 Architecture Guide
What it does
This skill enables AI agents to generate and visualize C4 (Context, Container, Component, and Code) diagrams for software systems. It translates complex codebases into structured visual representations that clarify system architecture at different levels of abstraction.
When to use it
- Onboard new developers by creating high-level Context diagrams to explain how your system fits within the broader ecosystem.
- Document microservices architectures using Container diagrams to illustrate interactions between services and external dependencies.
- Refactor legacy codebases by generating Component diagrams to map out internal logic and data flow without getting lost in implementation details.
- Prepare for technical interviews or stakeholder reviews by producing clear, standardized visual summaries of your application's structure.
Key capabilities
- Generates C4 diagrams across all four levels: Context, Container, Component, and Code.
- Automatically infers relationships between system components based on code analysis.
- Produces both static documentation and interactive diagram formats suitable for modern tech stacks.
- Supports multiple output formats including PlantUML, Mermaid, and standard image exports.
Example prompts
- "Analyze this React application and generate a C4 Container diagram showing how the frontend communicates with the backend API."
- "Create a C4 Component diagram for the authentication module in this Node.js service to highlight key classes and their interactions."
- "Produce a high-level C4 Context diagram explaining how our e-commerce platform integrates with third-party payment gateways and inventory systems."
Tips & gotchas
Ensure your codebase includes sufficient comments or documentation, as the skill relies on these to accurately infer architectural relationships. For large monolithic applications, consider breaking the analysis into smaller sub-systems to maintain diagram clarity and avoid overwhelming visual complexity.