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Pattern Analyzer

L3 Worker that analyzes a single architectural pattern against best practices and calculates 4 scores.

Purpose & Scope

• Analyze ONE pattern per invocation (receives pattern name, locations, best practices from coordinator)
• Find all implementations in codebase (Glob/Grep)
• Validate implementation exists and works
• Calculate 4 scores: compliance, completeness, quality, implementation
• Identify gaps and issues with severity and effort estimates
• Return structured analysis result to coordinator

Out of Scope (owned by ln-624-code-quality-auditor):

• Cyclomatic complexity thresholds (>10, >20)
• Method/class length thresholds (>50, >100, >500 lines)
• Quality Score focuses on pattern-specific quality (SOLID within pattern, pattern-level smells), not generic code metrics

Input (from ln-640 coordinator)

- pattern: string          # Pattern name (e.g., "Job Processing")
- locations: string[]      # Known file paths/directories
- bestPractices: object    # Best practices from MCP Ref/Context7/WebSearch

Note: All patterns arrive pre-verified (passed ln-640 Phase 1d applicability gate with >= 2 structural components confirmed).

Workflow

Phase 1: Find Implementations

MANDATORY READ: Load ../ln-640-pattern-evolution-auditor/references/pattern_library.md — use “Pattern Detection (Grep)” table for detection keywords per pattern.

IF pattern.source == "adaptive":
  # Pattern discovered by coordinator Phase 1b — evidence already provided
  files = pattern.evidence.files
  SKIP detection keyword search (already done in Phase 1b)
ELSE:
  # Baseline pattern — use library detection keywords
  files = Glob(locations)
  additional = Grep("{pattern_keywords}", "**/*.{ts,js,py,rb,cs,java}")
  files = deduplicate(files + additional)

Phase 2: Read and Analyze Code

FOR EACH file IN files (limit: 10 key files):
  Read(file)
  Extract: components, patterns, error handling, logging, tests

Phase 3: Calculate 4 Scores

MANDATORY READ: Load ../ln-640-pattern-evolution-auditor/references/scoring_rules.md — follow Detection column for each criterion.

Scoring process for each criterion:

1. Run the Detection Grep/Glob from scoring_rules.md
2. If matches found → add points per criterion
3. If anti-pattern/smell detected → subtract per deduction table
4. Document evidence: file path + line for each score justification

Phase 4: Identify Issues and Gaps

FOR EACH bestPractice NOT implemented:
  issues.append({
    severity: "HIGH" | "MEDIUM" | "LOW",
    category: "compliance" | "completeness" | "quality" | "implementation",
    issue: description,
    suggestion: how to fix,
    effort: "S" | "M" | "L"
  })

gaps = {
  missingComponents: required components not found in code,
  inconsistencies: conflicting or incomplete implementations
}

Phase 5: Calculate Overall Score

overall_score = average(compliance, completeness, quality, implementation) / 10

Phase 6: Return Result

{
  "pattern": "Job Processing",
  "overall_score": 7.9,
  "scores": {
    "compliance": 72,
    "completeness": 85,
    "quality": 68,
    "implementation": 90
  },
  "checks": [
    {"id": "compliance_check", "name": "Compliance", "status": "passed|warning|failed", "details": "..."},
    {"id": "completeness_check", "name": "Completeness", "status": "...", "details": "..."},
    {"id": "quality_check", "name": "Quality", "status": "...", "details": "..."},
    {"id": "implementation_check", "name": "Implementation", "status": "...", "details": "..."}
  ],
  "codeReferences": ["src/jobs/processor.ts", "src/workers/base.ts"],
  "issues": [
    {
      "severity": "HIGH",
      "category": "completeness",
      "issue": "No dead letter queue",
      "suggestion": "Add Bull DLQ configuration",
      "effort": "M"
    }
  ],
  "gaps": {
    "missingComponents": ["Dead letter queue"],
    "inconsistencies": ["Retry config exists but no backoff strategy"]
  },
  "recommendations": ["Add DLQ configuration for failed jobs"]
}

Critical Rules

• One pattern only: Analyze only the pattern passed by coordinator
• Read before score: Never score without reading actual code
• Detection-based scoring: Use Grep/Glob patterns from scoring_rules.md, not assumptions
• Effort estimates: Always provide S/M/L for each issue
• Code references: Always include file paths for findings

Definition of Done

• All implementations found via Glob/Grep (using pattern_library.md keywords or adaptive evidence)
• Key files read and analyzed
• 4 scores calculated using scoring_rules.md Detection patterns
• Issues identified with severity, category, suggestion, effort
• Gaps documented (missing components, inconsistencies)
• Recommendations provided
• Structured result returned to coordinator

Reference Files

• Scoring rules: ../ln-640-pattern-evolution-auditor/references/scoring_rules.md
• Pattern library: ../ln-640-pattern-evolution-auditor/references/pattern_library.md

Version: 2.0.0 Last Updated: 2026-02-08