
Coding
- 384 installs
- 4 repo stars
- Updated May 8, 2026
- davidkiss/smart-ai-skills
coding is a meta agent skill that enforces general software engineering best practices—DRY, KISS, and maintainability—for developers implementing features, fixing bugs, or refactoring across any codebase.
About
coding is a mandatory general-coding skill in davidkiss/smart-ai-skills that agents should load for all implementation, refactoring, and review tasks. It codifies core software engineering principles: DRY to avoid duplicated logic, KISS to prefer simple solutions, and practices for robust, maintainable, scalable code. The skill applies across frontend, backend, and test codebases without prescribing a specific framework, acting as a baseline quality guardrail when agents implement features or fix bugs. Developers reach for coding when they want agent-written code to follow consistent engineering discipline rather than ad-hoc shortcuts. It complements project-specific conventions in AGENTS.md or linters by steering architectural and readability decisions during generation. Because it spans the full implementation lifecycle, it pairs with narrower skills for testing, security, or deployment rather than replacing them.
- Cross-stack implementation guidance
- Bug fixes aligned to repo conventions
- Readable, maintainable code patterns
- Frontend and backend feature work
- Test-aware coding practices
Coding by the numbers
- 384 all-time installs (skills.sh)
- Ranked #114 of 782 Skill Development skills by installs in the Skillselion catalog
- Data as of Jul 29, 2026 (Skillselion catalog sync)
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| Installs | 384 |
|---|---|
| repo stars | ★ 4 |
| Last updated | May 8, 2026 |
| Repository | davidkiss/smart-ai-skills ↗ |
How do agents follow consistent coding best practices?
General coding assistance for implementing features, fixing bugs, and following project conventions across frontend, backend, and test codebases.
Who is it for?
Development teams wanting a default agent guardrail that enforces general software engineering principles on every coding task.
Skip if: Tasks needing framework-specific recipes, security audits, or deployment automation where a specialized skill replaces generic best-practice guidance.
When should I use this skill?
User starts any coding task—implement feature, refactor module, fix bug, or review code for quality and maintainability
What you get
Feature code, refactors, and bug fixes aligned with DRY, KISS, and maintainability engineering principles
- maintainable feature code
- refactored modules
Files
General Coding Best Practices
Overview
This skill provides a set of core principles and practices for software development. Use this when implementing new features, refactoring existing code, or reviewing code to ensure high quality and maintainability.
Core Principles
- DRY (Don't Repeat Yourself): Avoid logic duplication. If you find yourself writing the same code twice, abstract it.
- KISS (Keep It Simple, Stupid): Prefer simple, straightforward solutions over complex ones. Avoid over-engineering.
- YAGNI (You Ain't Gonna Need It): Don't implement features or abstractions until they are actually needed.
- SOLID Principles:
- Single Responsibility: A class/function should have one reason to change.
- Open/Closed: Software entities should be open for extension but closed for modification.
- Liskov Substitution: Subtypes must be substitutable for their base types.
- Interface Segregation: Many client-specific interfaces are better than one general-purpose interface.
- Dependency Inversion: Depend on abstractions, not concretions.
- Existing Guidelines: - Follow existing guidelines in the project (e.g. CLAUDE.md, AGENT.md, etc.)
Implementation Guidelines
- Clean Code: Use descriptive names for variables, functions, and classes. Write code that is easy to read and understand.
- Small Functions: Keep functions small and focused on a single task.
- Error Handling: Use proactive error handling. Validate inputs and handle exceptions gracefully.
- Documentation: Document the why, not the what. Use self-documenting code where possible.
- Security: Sanitize inputs, avoid hardcoding secrets, and follow the principle of least privilege.
- Performance: Be mindful of time and space complexity, but avoid premature optimization.
Automated Analysis & Quality Control
- Static Analysis & Linting: Every project MUST have automated linting, formatting and static analysis (e.g., ESLint, Prettier, Ruff, Sonar).
- Check: Identify if these tools are configured.
- Propose: If missing, immediately propose adding them (e.g.,
npm install --save-dev eslint). - Automated Tests: Ensure there is a test runner configured (e.g., Jest, Pytest).
- Check: Look for
tests/directory or test configurations inpackage.json/pyproject.toml. - Propose: If missing, propose a testing framework and initial setup.
Verifying Code Changes
Before completing any task, you MUST perform the following verification loop: 1. Simplification: Use the code-simplifier plugin to make the code cleaner and more maintainable. 2. Self-Code Review:
- Review the changes against the task requirements.
- Ensure compliance with this
codingskill (DRY, KISS, SOLID). - Check for potential security vulnerabilities or performance regressions.
3. Static Analysis & Linting:
- If project does not have linting/formatting configured, propose adding it.
- Run the project's linting/format commands (e.g.,
npm run lint,prettier --check .). - Fix all reported issues.
4. Unit Testing:
- If project does not have a test runner configured, propose adding one.
- Add Missing Tests: If new logic was added, write concise unit tests covering the happy path and edge cases.
- Run Tests: Execute the test suite (e.g.,
npm test,pytest). - Verification: Ensure all tests pass. If they fail, fix the implementation or the test.
Key Principles
- Clarity over Cleverness: Write code for humans first, machines second.
- Consistency: Follow the established patterns and style of the existing codebase.
- Composition over Inheritance: Prefer combining simple objects to build complex ones rather than creating deep inheritance hierarchies.
Related skills
FAQ
When should the coding skill activate?
The coding skill in davidkiss/smart-ai-skills must activate for all coding-related tasks including implementing features, refactoring existing code, fixing bugs, and reviewing code for maintainability and scalability.
What principles does the coding skill enforce?
The coding skill enforces DRY (Don't Repeat Yourself), KISS (Keep It Simple), and general practices for robust maintainable scalable software across frontend, backend, and test layers.