clean-architecture

pproenca/dot-skills · updated May 12, 2026

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$npx skills add https://github.com/pproenca/dot-skills --skill clean-architecture
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summary

42 Clean Architecture rules organized by priority for designing maintainable, testable software systems.

  • Covers 8 rule categories from dependency direction and entity design (critical) through testing architecture (low-medium), each with specific guidance and code examples
  • Dependency rules enforce inward-pointing dependencies, interface ownership, and acyclic component graphs to prevent architectural decay
  • Entity and use case rules isolate business logic from frameworks, persistence,
skill.md

Clean Architecture Best Practices

Comprehensive guide to Clean Architecture principles for designing maintainable, testable software systems. Based on Robert C. Martin's "Clean Architecture: A Craftsman's Guide to Software Structure and Design." Contains 42 rules across 8 categories, prioritized by architectural impact.

When to Apply

Reference these guidelines when:

  • Designing new software systems or modules
  • Structuring dependencies between layers
  • Defining boundaries between business logic and infrastructure
  • Reviewing code for architectural violations
  • Refactoring coupled systems toward cleaner structure

Rule Categories by Priority

Priority Category Impact Prefix
1 Dependency Direction CRITICAL dep-
2 Entity Design CRITICAL entity-
3 Use Case Isolation HIGH usecase-
4 Component Cohesion HIGH comp-
5 Boundary Definition MEDIUM-HIGH bound-
6 Interface Adapters MEDIUM adapt-
7 Framework Isolation MEDIUM frame-
8 Testing Architecture LOW-MEDIUM test-

Quick Reference

1. Dependency Direction (CRITICAL)

2. Entity Design (CRITICAL)

3. Use Case Isolation (HIGH)

4. Component Cohesion (HIGH)

5. Boundary Definition (MEDIUM-HIGH)

6. Interface Adapters (MEDIUM)

7. Framework Isolation (MEDIUM)

8. Testing Architecture (LOW-MEDIUM)

How to Use

Read individual reference files for detailed explanations and code examples:

Reference Files

File Description
references/_sections.md Category definitions and ordering
assets/templates/_template.md Template for new rules
metadata.json Version and reference information
how to use clean-architecture

How to use clean-architecture 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 clean-architecture
2

Execute installation command

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

$npx skills add https://github.com/pproenca/dot-skills --skill clean-architecture

The skills CLI fetches clean-architecture from GitHub repository pproenca/dot-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/clean-architecture

Reload or restart Cursor to activate clean-architecture. Access the skill through slash commands (e.g., /clean-architecture) 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

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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

4.765 reviews
  • Diego Thomas· Dec 28, 2024

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

  • Kiara Zhang· Dec 28, 2024

    clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Daniel Ndlovu· Dec 8, 2024

    clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kiara Yang· Dec 4, 2024

    clean-architecture reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Rahul Santra· Nov 27, 2024

    clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Diego Anderson· Nov 23, 2024

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

  • Kiara Chen· Nov 19, 2024

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

  • Kiara Menon· Nov 19, 2024

    clean-architecture has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Advait Diallo· Nov 15, 2024

    clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Pratham Ware· Oct 18, 2024

    clean-architecture has been reliable in day-to-day use. Documentation quality is above average for community skills.

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