Ai development guide

AI Developer Guide - Technical Decision Criteria and Anti-pattern Collection

Language-Specific References

For frontend-specific anti-patterns, debugging, and quality checks:

• React/TypeScript Frontend: references/frontend.md

Value-First Engineering [MANDATORY]

Explore broadly, then converge on the lowest-lifecycle-cost solution that delivers user value and leaves the system correct and maintainable.

• Resolve verified bugs, failing quality checks, and technical debt within confirmed scope or dependencies required for its outcome; report other findings separately.
• Introduce capabilities, infrastructure, abstractions, or speculative edge-case handling only when a current outcome, verified constraint, or evidence-backed material risk requires them.
• When resolving a known problem falls outside that boundary or materially changes behavior, architecture, or effort, obtain an explicit scope decision.

Technical Anti-patterns (Red Flag Patterns) [MANDATORY]

IMMEDIATELY stop and reconsider design when detecting the following patterns:

Code Quality Anti-patterns

1. Writing similar code 3 or more times - Violates Rule of Three
2. Multiple responsibilities mixed in a single file - Violates Single Responsibility Principle (SRP)
3. Maintaining the same runtime rule or data in multiple implementation sources when one source can safely serve all consumers - Creates synchronization risk
4. Making changes without checking dependencies - Potential for unexpected impacts
5. Disabling code with comments - Should use version control
6. Error suppression - Hiding problems creates technical debt
7. Bypassing safety mechanisms (type systems, validation, contracts) - Circumventing correctness guarantees

Design Anti-patterns

• "Make it work for now" thinking - Accumulation of technical debt
• Patchwork implementation - Unplanned additions to existing code
• Optimistic implementation of uncertain technology - EVALUATE unknown elements with minimal verification code first
• Symptomatic fixes - Identify root cause with 5 Whys instead of applying surface-level patches
• Unplanned large-scale changes - Use incremental approach with phased implementation

ENFORCEMENT: Detecting ANY anti-pattern requires IMMEDIATE design review before proceeding

Fail-Fast Fallback Design Principles

Core Principle [MANDATORY]

Make errors explicit with full context. Prioritize primary code reliability over fallback implementations. Silent fallbacks are PROHIBITED.

Implementation Guidelines

Default Approach [MANDATORY]

• Prohibit unconditional fallbacks: NEVER automatically return default values on errors
• Make failures explicit: Errors MUST be visible and traceable
• 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

Error Masking Detection

Review Triggers (require design review):

• Writing 3rd error handler in the same feature
• Multiple error handling blocks in single function/method
• Nested error handling structures
• Error handlers that return default values without logging

Before Implementing Any Fallback [MANDATORY]: STEP 1: Verify Design Doc explicitly defines this fallback STEP 2: Document the business justification STEP 3: Ensure error is logged with full context STEP 4: Add monitoring/alerting for fallback activation

ENFORCEMENT: Fallbacks without Design Doc approval are PROHIBITED

Rule of Three - Criteria for Code Duplication

How to handle duplicate code based on Martin Fowler's "Refactoring":

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

Pattern 5: Existing Code Investigation [MANDATORY]

Before implementation:

• Search for similar functionality
• Similar functionality found: Verify that it satisfies the current requirement and is representative of the repository; reuse or extend it when both checks pass
• Similar functionality is technical debt: Repair it when it blocks the current outcome, was caused by the current change, or lies in confirmed scope; otherwise report it separately. When the repair requires an architectural decision, record the decision in an ADR
• No suitable similar functionality: Implement using representative repository patterns
• When adopting a pattern or dependency from nearby code, verify it is representative across the repository before adopting it

Quality Assurance Mechanism Awareness

Before executing quality checks, discover applicable quality tools and constraints by inspecting the affected files' types, project manifests, CI pipelines, and configuration:

• Primary detection: inspect affected file types, manifests, configuration, and CI pipelines to identify applicable quality tools
• Check for domain-specific linters or validators such as schema validators, API spec validators, or configuration-file checkers
• Check for domain-specific constraints in project configuration such as naming rules, length limits, or format requirements
• When a task file lists Quality Assurance Mechanisms, use that section as supplementary guidance for what to verify
• Include discovered domain-specific checks alongside the standard quality phases below

Quality Check Workflow [MANDATORY]

Universal quality assurance phases applicable to all languages:

Phase 1: Static Analysis

1. Code Style Checking: Verify adherence to style guidelines
2. Code Formatting: Ensure consistent formatting
3. Unused Code Detection: Identify dead code and unused imports/variables
4. Static Type Checking: Verify type correctness (for statically typed languages)
5. Static Analysis: Detect potential bugs, security issues, code smells

Phase 2: Build Verification

1. Compilation/Build: Verify code builds successfully
2. Dependency Resolution: Ensure all dependencies are available and compatible
3. Resource Validation: Check configuration files, assets are valid

Phase 3: Testing

1. Unit Tests: Run all unit tests
2. Integration Tests: Run integration tests
3. Test Coverage: Measure coverage when configured and use it to find gaps
4. E2E Tests: Run end-to-end tests

Phase 4: Final Quality Gate [MANDATORY]

All checks MUST pass before proceeding:

• Zero static analysis errors
• Build succeeds
• All tests pass
• Coverage threshold passes when the project, task file, work plan, or Design Doc defines one. When no threshold is configured, use coverage output only to identify untested critical paths.

ENFORCEMENT: Cannot proceed with ANY quality check failures — fix ALL errors before marking task complete

Implementation Completeness Assurance

Impact Analysis: Mandatory 3-Stage Process [MANDATORY]

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 to transformations to destination)
• Coupling strength

3. Identification - Produce structured report:

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

### Indirect Impact
- [System]: [Integration path and 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. [...]

ENFORCEMENT: CANNOT implement until all 3 stages are documented

Unused Code Deletion

When unused code is detected:

• Within confirmed scope or dependencies required for the current outcome: Will it be used in this work? Yes: Implement now | No: Delete now (Git preserves)
• Outside that boundary: Report it separately for a scope decision
• Applies to: Code, tests, docs, configs, assets

Existing Code Modification

Within confirmed scope or required dependency?
  No  -> Report separately
  Yes -> In use?
           No  -> Delete
           Yes -> Working?
                    No  -> Delete + Reimplement
                    Yes -> Fix/Extend

Principle: Prefer clean implementation over patching broken code