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Code Quality Auditor (L3 Worker)

Specialized worker auditing code complexity, method signatures, algorithms, and constants management.

Purpose & Scope

• Worker in ln-620 coordinator pipeline - invoked by ln-620-codebase-auditor
• Audit code quality (Categories 5+6+NEW: Medium Priority)
• Check complexity metrics, method signature quality, algorithmic efficiency, constants management
• Return structured findings with severity, location, effort, recommendations
• Calculate compliance score (X/10) for Code Quality category

Inputs (from Coordinator)

MANDATORY READ: Load shared/references/task_delegation_pattern.md#audit-coordinator--worker-contract for contextStore structure.

Receives contextStore with: tech_stack, best_practices, principles, codebase_root, output_dir.

Domain-aware: Supports domain_mode + current_domain (see audit_output_schema.md#domain-aware-worker-output).

Workflow

1. Parse context — extract fields, determine scan_path (domain-aware if specified), extract output_dir

2. Scan codebase for violations

   • All Grep/Glob patterns use scan_path (not codebase_root)
   • Example: Grep(pattern="if.*if.*if", path=scan_path) for nesting detection

3. Collect findings with severity, location, effort, recommendation

   • Tag each finding with domain: domain_name (if domain-aware)

4. Calculate score using penalty algorithm

5. Write Report: Build full markdown report in memory per shared/templates/audit_worker_report_template.md, write to {output_dir}/624-quality-{domain}.md (or 624-quality.md in global mode) in single Write call

6. Return Summary: Return minimal summary to coordinator (see Output Format)

Audit Rules (Priority: MEDIUM)

1. Cyclomatic Complexity

What: Too many decision points in single function (> 10)

Detection:

• Count if/else, switch/case, ternary, &&, ||, for, while
• Use tools: eslint-plugin-complexity, radon (Python), gocyclo (Go)

Severity:

• HIGH: Complexity > 20 (extremely hard to test)
• MEDIUM: Complexity 11-20 (refactor recommended)
• LOW: Complexity 8-10 (acceptable but monitor)

Recommendation: Split function, extract helper methods, use early returns

Effort: M-L (depends on complexity)

2. Deep Nesting (> 4 levels)

What: Nested if/for/while blocks too deep

Detection:

• Count indentation levels
• Pattern: if { if { if { if { if { ... } } } } }

Severity:

• HIGH: > 6 levels (unreadable)
• MEDIUM: 5-6 levels
• LOW: 4 levels

Recommendation: Extract functions, use guard clauses, invert conditions

Effort: M (refactor structure)

3. Long Methods (> 50 lines)

What: Functions too long, doing too much

Detection:

• Count lines between function start and end
• Exclude comments, blank lines

Severity:

• HIGH: > 100 lines
• MEDIUM: 51-100 lines
• LOW: 40-50 lines (borderline)

Recommendation: Split into smaller functions, apply Single Responsibility

Effort: M (extract logic)

4. God Classes/Modules (> 500 lines)

What: Files with too many responsibilities

Detection:

• Count lines in file (exclude comments)
• Check number of public methods/functions

Severity:

• HIGH: > 1000 lines
• MEDIUM: 501-1000 lines
• LOW: 400-500 lines

Recommendation: Split into multiple files, apply separation of concerns

Effort: L (major refactor)

5. Too Many Parameters (> 5)

What: Functions with excessive parameters

Detection:

• Count function parameters
• Check constructors, methods

Severity:

• MEDIUM: 6-8 parameters
• LOW: 5 parameters (borderline)

Recommendation: Use parameter object, builder pattern, default parameters

Effort: S-M (refactor signature + calls)

6. O(n²) or Worse Algorithms

What: Inefficient nested loops over collections

Detection:

• Nested for loops: for (i) { for (j) { ... } }
• Nested array methods: arr.map(x => arr.filter(...))

Severity:

• HIGH: O(n²) in hot path (API request handler)
• MEDIUM: O(n²) in occasional operations
• LOW: O(n²) on small datasets (n < 100)

Recommendation: Use hash maps, optimize with single pass, use better data structures

Effort: M (algorithm redesign)

7. N+1 Query Patterns

What: ORM lazy loading causing N+1 queries

Detection:

• Find loops with database queries inside
• Check ORM patterns: users.forEach(u => u.getPosts())

Severity:

• CRITICAL: N+1 in API endpoint (performance disaster)
• HIGH: N+1 in frequent operations
• MEDIUM: N+1 in admin panel

Recommendation: Use eager loading, batch queries, JOIN

Effort: M (change ORM query)

8. Constants Management (NEW)

What: Magic numbers/strings, decentralized constants, duplicates

Detection:

Issue	Pattern	Example
Magic numbers	Hardcoded numbers in conditions/calculations	if (status === 2)
Magic strings	Hardcoded strings in comparisons	if (role === 'admin')
Decentralized	Constants scattered across files	MAX_SIZE = 100 in 5 files
Duplicates	Same value multiple times	STATUS_ACTIVE = 1 in 3 places
No central file	Missing constants.ts or config.py	No single source of truth

Severity:

• HIGH: Magic numbers in business logic (payment amounts, statuses)
• MEDIUM: Duplicate constants (same value defined 3+ times)
• MEDIUM: No central constants file
• LOW: Magic strings in logging/debugging

Recommendation:

• Create central constants file (constants.ts, config.py, constants.go)
• Extract magic numbers to named constants: const STATUS_ACTIVE = 1
• Consolidate duplicates, import from central file
• Use enums for related constants

Effort: M (extract constants, update imports, consolidate)

9. Method Signature Quality

What: Poor method contracts reducing readability and maintainability

Detection:

Issue	Pattern	Example
Boolean flag params	>=2 boolean params in signature	def process(data, is_async: bool, skip_validation: bool)
Too many optional params	>=3 optional params with defaults	def query(db, limit=10, offset=0, sort="id", order="asc")
Inconsistent verb naming	Different verbs for same operation type in one module	get_user() vs fetch_account() vs load_profile()
Unclear return type	-> dict, -> Any, -> tuple without TypedDict/NamedTuple	def get_stats() -> dict instead of -> StatsResponse

Severity:

• MEDIUM: Boolean flag params (use enum/strategy), unclear return types
• LOW: Too many optional params, inconsistent naming

Recommendation:

• Boolean flags: replace with enum, strategy pattern, or separate methods
• Optional params: group into config/options dataclass
• Naming: standardize verb conventions per module (get_ for sync, fetch_ for async, etc.)
• Return types: use TypedDict, NamedTuple, or dataclass instead of raw dict/tuple

Effort: S-M (refactor signatures + callers)

10. Side-Effect Cascade Depth

What: Functions triggering cascading chains of external side-effects (DB writes → notifications → metrics → limits).

Detection: MANDATORY READ: shared/references/ai_ready_architecture.md for side-effect markers, false positive exclusions, and opaque sink rules.

• Glob **/services/**/*.{py,ts,js,cs,java} to find service files
• For each public function: check body for side-effect markers (per reference)
• Recursively follow called internal functions for additional markers
• Calculate max chain depth from entry point

Severity:

• HIGH: cascade_depth >= 4
• MEDIUM: cascade_depth = 3
• OK: depth <= 2

Recommendation: Refactor to flat orchestration — extract side-effects into independent sink functions. See reference.

Effort: M-L

Output: Also generate summary Pipe/Sink table per module:

Module	Sinks (0-1)	Shallow Pipes (2)	Deep Pipes (3+)	Sink Ratio

Scoring Algorithm

MANDATORY READ: Load shared/references/audit_scoring.md for unified scoring formula.

Output Format

MANDATORY READ: Load shared/templates/audit_worker_report_template.md for file format.

Write report to {output_dir}/624-quality-{domain}.md (or 624-quality.md in global mode) with category: "Code Quality" and checks: cyclomatic_complexity, deep_nesting, long_methods, god_classes, too_many_params, quadratic_algorithms, n_plus_one, magic_numbers, method_signatures, cascade_depth.

Return summary to coordinator:

Report written: docs/project/.audit/ln-620/{YYYY-MM-DD}/624-quality-orders.md
Score: X.X/10 | Issues: N (C:N H:N M:N L:N)

Critical Rules

• Do not auto-fix: Report only
• Domain-aware scanning: If domain_mode="domain-aware", scan ONLY scan_path (not entire codebase)
• Tag findings: Include domain field in each finding when domain-aware
• Context-aware: Small functions (n < 100) with O(n²) may be acceptable
• Constants detection: Exclude test files, configs, examples
• Metrics tools: Use existing tools when available (ESLint complexity plugin, radon, gocyclo)

Definition of Done

• contextStore parsed (including domain_mode, current_domain, output_dir)
• scan_path determined (domain path or codebase root)
• All 10 checks completed (scoped to scan_path):
  • complexity, nesting, length, god classes, parameters, O(n²), N+1, constants, method signatures, cascade depth
• Findings collected with severity, location, effort, recommendation, domain
• Score calculated
• Report written to {output_dir}/624-quality-{domain}.md (atomic single Write call)
• Summary returned to coordinator

Reference Files

• Worker report template: shared/templates/audit_worker_report_template.md
• Audit scoring formula: shared/references/audit_scoring.md
• Audit output schema: shared/references/audit_output_schema.md
• Code quality rules: references/code_quality_rules.md

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Version: 3.0.0 Last Updated: 2025-12-23