Agent skill · Design & Presentation

architecture-design

Use only when creating new registrable ML components that require Factory or Registry patterns.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeships scriptsNOASSERTION
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill architecture-design --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 9 KB
Bundled scripts: yes
Version: 1.2.0
Path: skills/33-Galaxy-Dawn-claude-scholar/skills/architecture-design/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,244
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Architecture Design - ML Project Template This skill defines the standard code architecture for machine learning projects based on the template structure. When modifying or extending code, follow these patterns to maintain consistency. ## Overview The project follows a modular, extensible architecture with clear separation of concerns. Each module (data, model, trainer, analysis) is independentl

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About this skill
What does the architecture-design skill do?

Use only when creating new registrable ML components that require Factory or Registry patterns.

How do I install it?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill architecture-design --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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