Standardize code style, comments, and clean-code decisions for source code generation and review.
npx skills add https://github.com/drgon1/santian --skill awesome-rules
Please help me install this Agent Skill. Skill: awesome-rules Source: https://github.com/drgon1/santian/tree/main/ai-skills/skills/awesome-rules Command: npx skills add https://github.com/drgon1/santian --skill awesome-rules
This Skill removes inconsistency in code generation by giving AI a unified set of rules for naming, comments, structure, error handling, and clean-code decisions.
Apply the awesome-rules skill when writing or reviewing code so the result follows its naming, commenting, and clean-code standards.
High-intent search queries and answers about installing and using this skill.
To enforce clean code and SOLID principles during AI code generation, apply standardized rules for naming conventions, single-responsibility functions, and defensive error handling to ensure consistent, readable output across your project.
The best way to standardize Python code style and comments is to apply unified rules that enforce descriptive names, concise comments explaining why decisions were made, DRY structures, and clean module organization throughout the codebase.
To write code comments that explain why, apply rules that keep comments concise and meaningful, focusing on the reasoning behind clean-code decisions and defensive error handling rather than simply describing the implementation logic.
Yes, you can use automated rules to review code for SRP, OCP, and DIP alignment by inspecting generated or refactored source code against SOLID-oriented implementation tasks and clean module organization standards.
Applying strict naming conventions helps with refactoring large codebases by enforcing descriptive names and small single-responsibility functions, which maintains DRY structure and improves overall readability and maintainability.
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