architecture-decision▌
jwynia/agent-skills · updated May 1, 2026
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Systematically evaluate architecture decisions, document trade-offs, and select appropriate patterns for context. Provides frameworks for pattern selection, ADR creation, and technical debt management.
Architecture Decision
Systematically evaluate architecture decisions, document trade-offs, and select appropriate patterns for context. Provides frameworks for pattern selection, ADR creation, and technical debt management.
When to Use This Skill
Use this skill when:
- Making technology choices
- Evaluating architectural patterns
- Creating Architecture Decision Records
- Assessing technical debt
- Comparing design alternatives
Do NOT use this skill when:
- Writing implementation code
- Working on requirements (use requirements-analysis)
- Doing full system design (use system-design)
Core Principle
Context drives decisions. No pattern is universally good or bad. The best architecture is not the most elegant—it's the one that best serves its purpose while remaining maintainable and evolvable.
The Trade-off Triangle
Every architectural decision involves trade-offs:
| Vertex | Maximized By | Cost |
|---|---|---|
| Simplicity | Monolith, sync communication, single DB | Scalability limits |
| Flexibility | Microservices, event-driven, plugins | Complexity overhead |
| Performance | Caching, denormalization, optimized code | Maintainability |
Balance Strategies:
- Start simple, add complexity as needed
- Measure before optimizing
- Use abstractions to defer decisions
- Evolve incrementally
Quality Attributes
Performance
- Metrics: Response time (p50, p95, p99), throughput, resource utilization
- Tactics: Caching, load balancing, async processing
Scalability
- Dimensions: Horizontal, vertical, elastic
- Patterns: Stateless services, sharding, event streaming
Reliability
- Metrics: Uptime, MTBF, MTTR
- Patterns: Circuit breakers, retries, redundancy
Maintainability
- Factors: Readability, modularity, testability
- Patterns: Clean architecture, DDD, SOLID
Context-Pattern Mapping
Team Context
| Context | Preferred Patterns | Avoid |
|---|---|---|
| Small team | Monolith, vertical slices, shared DB | Microservices, complex abstractions |
| Multiple teams | Service boundaries, API contracts | Shared state, tight coupling |
Scale Context
| Context | Preferred Patterns | Reasoning |
|---|---|---|
| Startup | Monolith first, vertical scaling | Optimize for development speed |
| Enterprise | Service mesh, horizontal scaling | Optimize for operational scale |
Decision Matrix Template
| Option | Consistency | Flexibility | Scalability | Complexity | Cost | Total |
|---|---|---|---|---|---|---|
| Option A | 5 | 2 | 3 | 2 | 3 | 15 |
| Option B | 3 | 5 | 4 | 3 | 3 | 18 |
| Option C | 2 | 3 | 5 | 1 | 2 | 13 |
Weight factors based on context priorities.
Architecture Decision Record (ADR) Template
# ADR-[NUMBER]: [TITLE]
## Status
[Proposed | Accepted | Deprecated | Superseded]
## Context
[What is the situation requiring a decision?]
### Requirements
- [Requirement 1]
- [Requirement 2]
### Constraints
- [Constraint 1]
- [Constraint 2]
## Decision
[What is the decision?]
### Justification
- [Reason 1]
- [Reason 2]
## Consequences
### Positive
- [Benefit 1]
- [Benefit 2]
### Negative
- [Drawback 1]
- [Drawback 2]
## Alternatives Considered
### [Alternative 1]
Reason rejected: [Why]
### [Alternative 2]
Reason rejected: [Why]
Architectural Refactoring Patterns
Branch by Abstraction
- Create abstraction over current implementation
- Implement new solution behind abstraction
- Switch to new implementation
- Remove old implementation
Strangler Fig
- Identify boundary
- Implement new solution for new features
- Gradually migrate old features
- Retire old system
Parallel Run
- Implement new solution
- Run both old and new
- Compare results
- Switch when confident
Technical Debt Management
Debt Categories
| Type | Examples | Payment Strategy |
|---|---|---|
| Design | Missing abstractions, tight coupling | Refactoring sprints |
| Code | Duplication, complexity, poor naming | Continuous cleanup |
| Test | Missing tests, flaky tests | Test improvement |
| Documentation | Missing docs, outdated diagrams | Documentation sprints |
Metrics
- Debt ratio: Debt work / Total work (target < 20%)
- Interest rate: Extra effort due to debt
- Debt ceiling: Maximum acceptable debt
Anti-Patterns
Big Ball of Mud
Symptoms: No clear structure, everything depends on everything Remedy: Identify boundaries, extract modules, establish interfaces
Distributed Monolith
Symptoms: Services must deploy together, sync chains, shared DBs Remedy: Merge related services, async communication, separate DBs
Golden Hammer
Symptoms: One solution for all problems, force-fitting patterns Remedy: Learn alternatives, evaluate objectively, prototype options
Related Skills
- system-design - Full system design with ADRs
- code-review - Implementation validation
- task-decomposition - Breaking down architectural work
- requirements-analysis - Understanding constraints
How to use architecture-decision on Cursor
AI-first code editor with Composer
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 architecture-decision
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches architecture-decision from GitHub repository jwynia/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate architecture-decision. Access the skill through slash commands (e.g., /architecture-decision) 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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★43 reviews- ★★★★★Ganesh Mohane· Dec 28, 2024
Useful defaults in architecture-decision — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Isabella Flores· Dec 28, 2024
We added architecture-decision from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Shikha Mishra· Dec 24, 2024
Solid pick for teams standardizing on skills: architecture-decision is focused, and the summary matches what you get after install.
- ★★★★★Luis Ndlovu· Dec 24, 2024
architecture-decision fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Ghosh· Dec 12, 2024
Registry listing for architecture-decision matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Nov 19, 2024
architecture-decision is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Isabella Rao· Nov 19, 2024
architecture-decision reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Emma Chawla· Nov 15, 2024
Registry listing for architecture-decision matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Jain· Nov 3, 2024
architecture-decision fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Isabella Ghosh· Oct 22, 2024
We added architecture-decision from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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