This skill identifies anti-patterns that violate amplihack's development philosophy and provides constructive, specific fixes. It ensures code maintains ruthless simplicity, modular design, and zero-BS implementations.
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This skill identifies anti-patterns that violate amplihack's development philosophy and provides constructive, specific fixes. It ensures code maintains ruthless simplicity, modular design, and zero-BS implementations.
When to Use This Skill
Code review: Identify violations before merging
Refactoring: Find opportunities to simplify and improve code quality
New module creation: Catch issues early in development
Philosophy compliance: Ensure code aligns with amplihack principles
Learning: Understand why patterns are problematic and how to fix them
Mentoring: Educate team members on philosophy-aligned code patterns
Core Philosophy Reference
Amplihack Development Philosophy focuses on:
Ruthless Simplicity: Every abstraction must justify its existence
What It Is: Functions that do too many things and are difficult to understand, test, and modify.
Why It's Bad: Violates single responsibility, makes testing harder, increases bug surface area, reduces code reusability.
Red Flags:
Functions with >50 lines of code
Multiple indentation levels (3+ nested if/for)
Functions with 5+ parameters
Functions that need scrolling to see all of them
Complex logic that's hard to name
Example - SMELL:
# BAD: Large function doing multiple thingsdefprocess_user_data(user_dict, validate=True, save=True, notify=True, log=True):if validate:ifnot user_dict.get('email'):raise ValueError("Email required")ifnot'@'in user_dict['email']:raise ValueError("Invalid email") user = User( name=user_dict['name'], email=user_dict['email'], phone=user_dict['phone'])if save: db.save(user)if notify: email_service.send(user.email,"Welcome!")if log: logger.info(f"User {user.name} created")# ... 30+ more lines of mixed concernsreturn user
Example - FIXED:
# GOOD: Separated concernsdefvalidate_user_data(user_dict):"""Validate user data structure."""ifnot user_dict.get('email'):raise ValueError("Email required")if'@'notin user_dict['email']:raise ValueError("Invalid email")defcreate_user(user_dict):"""Create user object from data."""return User( name=user_dict['name'], email=user_dict['email'], phone=user_dict['phone'])defprocess_user_data(user_dict):"""Orchestrate user creation workflow.""" validate_user_data(user_dict) user = create_user(user_dict) db.save(user) email_service.send(user.email,"Welcome!") logger.info(f"User {user.name} created")return user
Detection Checklist:
Function body >50 lines
3+ levels of nesting
Multiple unrelated operations
Hard to name succinctly
5+ parameters
Fix Strategy:
Extract helper functions for each concern
Give each function a clear, single purpose
Compose small functions into larger workflows
Each function should fit on one screen
Easy to name = usually doing one thing
4. Tight Coupling
What It Is: Modules/classes directly depend on concrete implementations instead of abstractions, making them hard to test and modify.
Why It's Bad: Changes in one module break others. Hard to test in isolation. Violates modularity principle.
Red Flags:
Direct instantiation of classes inside functions (db = Database())
Deep attribute access (obj.service.repository.data)
βΊAccess to product documentation and roadmap tools (Jira, Notion, etc.)
βΊUnderstanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
βΊStakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
β Not validating competitive researchβverify facts before sharing
β Accepting user stories without involving engineering team
β Over-relying on frameworks without qualitative judgment
β Not customizing outputs to company culture and communication style
β Skipping stakeholder validation of generated requirements
Best Practices
β 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
β Don't
βDon't publish competitive analysis without fact-checking
βDon't finalize user stories without engineering review
βDon't make prioritization decisions solely on AI scoring
βDon't skip customer validation of generated requirements
βDon't ignore company-specific context and culture
π‘ Pro Tips
β Provide context: company goals, constraints, customer feedback
β Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
β Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
β Use skill for 70% generation + 30% customization to company needs
When to Use This
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β 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