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
-
Premature Optimization
- Caching for rarely accessed data
- Performance optimizations without benchmarks
- Complex algorithms for small datasets
- Micro-optimizations before profiling
-
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)
-
Over-abstraction
- Generic solutions for specific problems
- Too many configuration options
- Unnecessary plugin/extension systems
- Abstract classes with single implementations
-
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
-
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.