coding

davidkiss/smart-ai-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/davidkiss/smart-ai-skills --skill coding
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summary

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.

skill.md

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 in package.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 coding skill (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.
how to use coding

How to use coding on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add coding
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davidkiss/smart-ai-skills --skill coding

The skills CLI fetches coding from GitHub repository davidkiss/smart-ai-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/coding

Reload or restart Cursor to activate coding. Access the skill through slash commands (e.g., /coding) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.875 reviews
  • Hiroshi Rao· Dec 28, 2024

    Useful defaults in coding — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Anika Harris· Dec 24, 2024

    I recommend coding for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Isabella Patel· Dec 24, 2024

    Solid pick for teams standardizing on skills: coding is focused, and the summary matches what you get after install.

  • Kiara Perez· Dec 24, 2024

    coding reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Luis Thomas· Dec 20, 2024

    I recommend coding for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 23, 2024

    Registry listing for coding matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hiroshi Srinivasan· Nov 19, 2024

    coding is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hiroshi Tandon· Nov 15, 2024

    Keeps context tight: coding is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Charlotte Rahman· Nov 15, 2024

    We added coding from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Isabella Dixit· Nov 11, 2024

    Keeps context tight: coding is the kind of skill you can hand to a new teammate without a long onboarding doc.

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