Specification Completeness Check

Analyze the specification at: $ARGUMENTS

Analysis Framework

This command will analyze the provided specification document to determine if it contains sufficient detail for successful autonomous implementation, while also identifying overengineering and non-essential complexity that should be removed or deferred.

Domain Expertise

When analyzing specifications that involve specific technical domains, leverage available skills and knowledge:

• Apply domain-specific best practices (TypeScript patterns, React conventions, database design, etc.)
• Reference project-specific conventions from CLAUDE.md or similar documentation
• Use the Explore agent sparingly for deep codebase analysis when needed

What This Check Evaluates

The analysis evaluates three fundamental aspects, each with specific criteria:

1. WHY - Intent and Purpose

• Background/Problem Statement clarity
• Goals and Non-Goals definition
• User value/benefit explanation
• Justification vs alternatives
• Success criteria

2. WHAT - Scope and Requirements

• Features and functionality definition
• Expected deliverables
• API contracts and interfaces
• Data models and structures
• Integration requirements:
  • External system interactions?
  • Authentication mechanisms?
  • Communication protocols?
• Performance requirements
• Security requirements

3. HOW - Implementation Details

• Architecture and design patterns
• Implementation phases/roadmap
• Technical approach:
  • Core logic and algorithms
  • All functions and methods fully specified?
  • Execution flow clearly defined?
• Error handling:
  • All failure modes identified?
  • Recovery behavior specified?
  • Edge cases documented?
• Platform considerations:
  • Cross-platform compatibility?
  • Platform-specific implementations?
  • Required dependencies per platform?
• Resource management:
  • Performance constraints defined?
  • Resource limits specified?
  • Cleanup procedures documented?
• Testing strategy:
  • Test purpose documentation (each test explains why it exists)
  • Meaningful tests that can fail to reveal real issues
  • Edge case coverage and failure scenarios
  • Follows project testing philosophy: "When tests fail, fix the code, not the test"
• Deployment considerations

Additional Quality Checks

Completeness Assessment

• Missing critical sections
• Unresolved decisions
• Open questions

Clarity Assessment

• Ambiguous statements
• Assumed knowledge
• Inconsistencies

Overengineering Assessment

• Features not aligned with core user needs
• Premature optimizations
• Unnecessary complexity patterns

Overengineering Detection

Core Value Alignment Analysis

Evaluate whether features directly serve the core user need:

• Does this feature solve a real, immediate problem?
• Is it being used frequently enough to justify complexity?
• Would a simpler solution work for 80% of use cases?

YAGNI Principle (You Aren't Gonna Need It)

Be aggressive about cutting features:

• If unsure whether it's needed -> Cut it
• If it's for "future flexibility" -> Cut it
• If only 20% of users need it -> Cut it
• If it adds any complexity -> Question it, probably cut it

Common Overengineering Patterns to Detect

1. Premature Optimization

   • Caching for rarely accessed data
   • Performance optimizations without benchmarks
   • Complex algorithms for small datasets
   • Micro-optimizations before profiling

2. Feature Creep

   • "Nice to have" features (cut them)
   • Edge case handling for unlikely scenarios (cut them)
   • Multiple ways to do the same thing (keep only one)
   • Features that "might be useful someday" (definitely cut)

3. Over-abstraction

   • Generic solutions for specific problems
   • Too many configuration options
   • Unnecessary plugin/extension systems
   • Abstract classes with single implementations

4. Infrastructure Overhead

   • Complex build pipelines for simple tools
   • Multiple deployment environments for internal tools
   • Extensive monitoring for non-critical features
   • Database clustering for low-traffic applications

5. Testing Extremism

   • 100% coverage requirements
   • Testing implementation details
   • Mocking everything
   • Edge case tests for prototype features

Simplification Recommendations

• Identify features to cut from the spec entirely
• Suggest simpler alternatives
• Highlight unnecessary complexity
• Recommend aggressive scope reduction to core essentials

Output Format

The analysis will provide:

• Summary: Overall readiness assessment (Ready/Not Ready)
• Critical Gaps: Must-fix issues blocking implementation
• Missing Details: Specific areas needing clarification
• Risk Areas: Potential implementation challenges
• Overengineering Analysis:
  • Non-core features that should be removed entirely
  • Complexity that doesn't align with usage patterns
  • Suggested simplifications or complete removal
• Features to Cut: Specific items to remove from the spec
• Essential Scope: Absolute minimum needed to solve the core problem
• Recommendations: Next steps to improve the spec

Example Overengineering Detection

When analyzing a specification, the validator might identify patterns like:

Example 1: Unnecessary Caching

• Spec includes: "Cache user preferences with Redis"
• Analysis: User preferences accessed once per session
• Recommendation: Use in-memory storage or browser localStorage for MVP

Example 2: Premature Edge Cases

• Spec includes: "Handle 10,000+ concurrent connections"
• Analysis: Expected usage is <100 concurrent users
• Recommendation: Cut this entirely - let it fail at scale if needed

Example 3: Over-abstracted Architecture

• Spec includes: "Plugin system for custom validators"
• Analysis: Only 3 validators needed, all known upfront
• Recommendation: Implement validators directly, no plugin system needed

Example 4: Excessive Testing Requirements

• Spec includes: "100% code coverage with mutation testing"
• Analysis: Tool used occasionally, not mission-critical
• Recommendation: Focus on core functionality tests (70% coverage)

Example 5: Feature Creep

• Spec includes: "Support 5 export formats (JSON, CSV, XML, YAML, TOML)"
• Analysis: 95% of users only need JSON
• Recommendation: Cut all formats except JSON - YAGNI (You Aren't Gonna Need It)

This comprehensive analysis helps ensure specifications are implementation-ready while keeping scope focused on core user needs, reducing both ambiguity and unnecessary complexity.