ai-development-guide

name	ai-development-guide
description	Detects code smells, anti-patterns, and debugging issues. Use when: fixing bugs, reviewing code quality, or refactoring.

AI Developer Guide

Technical Anti-patterns (Red Flag Patterns)

Immediately stop and reconsider design when detecting the following patterns:

Code Quality Anti-patterns

1. Writing similar code 3 or more times
2. Multiple responsibilities mixed in a single file
3. Defining same content in multiple files
4. Making changes without checking dependencies
5. Disabling code with comments
6. Error suppression

Design Anti-patterns

• "Make it work for now" thinking
• Patchwork implementation
• Optimistic implementation of uncertain technology
• Symptomatic fixes
• Unplanned large-scale changes

Fail-Fast Fallback Design Principles

Core Principle

Prioritize primary code reliability over fallback implementations. In distributed systems, excessive fallback mechanisms can mask errors and make debugging difficult.

Implementation Guidelines

Default Approach

• Explicit failure over silent defaults: Errors must be visible and traceable, not masked by automatic default values
• Preserve error context: Include original error information when re-throwing

When Fallbacks Are Acceptable

• Only with explicit Design Doc approval: Document why fallback is necessary
• Business-critical continuity: When partial functionality is better than none
• Graceful degradation paths: Clearly defined degraded service levels

Layer Responsibilities

• Infrastructure Layer:

  • Always throw errors upward
  • No business logic decisions
  • Provide detailed error context

• Application Layer:

  • Make business-driven error handling decisions
  • Implement fallbacks only when specified in requirements
  • Log all fallback activations for monitoring

Error Masking Detection

Review Triggers (require design review):

• Writing 3rd error handling block in the same feature
• Multiple error handling structures in single function
• Nested error handling structures
• Error handlers that return default values without propagating

Before Implementing Any Fallback:

1. Verify Design Doc explicitly defines this fallback
2. Document the business justification
3. Ensure error is logged with full context
4. Add monitoring/alerting for fallback activation

Implementation Patterns

Note: Use your language's standard error handling mechanism (exceptions, Result types, error values, etc.)

❌ AVOID: Silent fallback that hides errors
    [handle error]:
        return DEFAULT_USER  // Error is hidden, debugging becomes difficult

✅ PREFERRED: Explicit failure with context
    [handle error]:
        logError('Failed to fetch user data', userId, error)
        propagate ServiceError('User data unavailable', error)

✅ ACCEPTABLE: Documented fallback with monitoring (when justified in Design Doc)
    [handle error]:
        // Fallback defined in Design Doc section 3.2.1
        logWarning('Primary data source failed, using cache', error)
        incrementMetric('data.fallback.cache_used')

        cachedData = fetchFromCache()
        if not cachedData:
            propagate ServiceError('Both primary and cache failed', error)
        return cachedData

Rule of Three - Criteria for Code Duplication

Duplication Count	Action	Reason
1st time	Inline implementation	Cannot predict future changes
2nd time	Consider future consolidation	Pattern beginning to emerge
3rd time	Implement commonalization	Pattern established

Criteria for Commonalization

Cases for Commonalization

• Business logic duplication
• Complex processing algorithms
• Areas likely requiring bulk changes
• Validation rules

Cases to Avoid Commonalization

• Accidental matches (coincidentally same code)
• Possibility of evolving in different directions
• Significant readability decrease from commonalization
• Simple helpers in test code

Implementation Example

// ❌ Bad: Immediate commonalization on 1st duplication
function validateUserEmail(email) { /* ... */ }
function validateContactEmail(email) { /* ... */ }
// → Premature abstraction

// ✅ Good: Commonalize on 3rd occurrence
// 1st time: inline implementation
// 2nd time: Copy but consider future
// 3rd time: Extract to common validator
function validateEmail(email, context) { /* ... */ }

Common Failure Patterns and Avoidance Methods

Pattern 1: Error Fix Chain

Symptom: Fixing one error causes new errors Cause: Surface-level fixes without understanding root cause Avoidance: Identify root cause with 5 Whys before fixing

Pattern 2: Implementation Without Sufficient Testing

Symptom: Many bugs after implementation Cause: Ignoring Red-Green-Refactor process Avoidance: Always start with failing tests

Pattern 4: Ignoring Technical Uncertainty

Symptom: Frequent unexpected errors when introducing new technology Cause: Assuming "it should work according to official documentation" without prior investigation Avoidance:

• Record certainty evaluation at the beginning of task files

  Certainty: low (Reason: no examples of MCP connection found)
  Exploratory implementation: true
  Fallback: use conventional API
  

• For low certainty cases, create minimal verification code first

Pattern 5: Insufficient Existing Code Investigation

Symptom: Duplicate implementations, architecture inconsistency, integration failures Cause: Insufficient understanding of existing code before implementation Avoidance Methods:

• Before implementation, always search for similar functionality (using domain, responsibility, configuration patterns as keywords)
• Similar functionality found → Use that implementation (do not create new implementation)
• Similar functionality is technical debt → Create ADR improvement proposal before implementation
• No similar functionality exists → Implement new functionality following existing design philosophy
• Record all decisions and rationale in "Existing Codebase Analysis" section of Design Doc

Debugging Techniques

1. Error Analysis Procedure

# How to read stack traces
1. Read error message (first line) accurately
2. Focus on first and last of stack trace
3. Identify first line where your code appears

2. 5 Whys - Root Cause Analysis

Symptom: Application crash on startup
Why1: Configuration loading failed → Why2: Config file format changed
Why3: Dependency update → Why4: Library breaking change
Why5: Unconstrained dependency version specification
Root cause: Inappropriate version management strategy

3. Minimal Reproduction Code

To isolate problems, attempt reproduction with minimal code:

• Remove unrelated parts
• Replace external dependencies with mocks
• Create minimal configuration that reproduces problem

4. Debug Log Output

// Track problems with structured logs
log('DEBUG:', {
  context: 'user-creation',
  input: { email, name },
  state: currentState,
  timestamp: currentTimestamp()
})

Situations Requiring Technical Decisions

Timing of Abstraction

• Extract patterns after writing concrete implementation 3 times
• Be conscious of YAGNI, implement only currently needed features
• Prioritize current simplicity over future extensibility

Performance vs Readability

• Prioritize readability unless clear bottleneck exists
• Measure before optimizing (don't guess, measure)
• Document reason with comments when optimizing

Continuous Improvement Mindset

• Humility: Perfect code doesn't exist, welcome feedback
• Courage: Execute necessary refactoring boldly
• Transparency: Clearly document technical decision reasoning

Implementation Completeness Assurance

Impact Analysis: Mandatory 3-Stage Process

Complete these stages sequentially before any implementation:

1. Discovery - Identify all affected code:

• Implementation references (imports, calls, instantiations)
• Interface dependencies (contracts, types, data structures)
• Test coverage
• Configuration (build configs, env settings, feature flags)
• Documentation (comments, docs, diagrams)

2. Understanding - Analyze each discovered location:

• Role and purpose in the system
• Dependency direction (consumer or provider)
• Data flow (origin → transformations → destination)
• Coupling strength

3. Identification - Produce structured report:

## Impact Analysis
### Direct Impact
- [Unit]: [Reason and modification needed]

### Indirect Impact
- [System]: [Integration path → reason]

### Data Flow
[Source] → [Transformation] → [Consumer]

### Risk Assessment
- High: [Complex dependencies, fragile areas]
- Medium: [Moderate coupling, test gaps]
- Low: [Isolated, well-tested areas]

### Implementation Order
1. [Start with lowest risk or deepest dependency]
2. [...]

Critical: Do not implement until all 3 stages are documented

Relationship to Pattern 5: This process provides the structured methodology to avoid "Insufficient Existing Code Investigation"

Unused Code Deletion

When unused code is detected:

• Will it be used in this work? Yes → Implement now | No → Delete now (Git preserves)
• Applies to: Code, tests, docs, configs, assets

Existing Code Modification

In use? No → Delete
       Yes → Working? No → Delete + Reimplement
                     Yes → Fix/Extend

Principle: Prefer clean implementation over patching broken code