# Diff Quality Analyzer > Implements intelligent diff quality analyzer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/diff-quality-analyzer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/diff-quality-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/diff-quality-analyzer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/diff-quality-analyzer --- # Diff Quality Analyzer Orchestrates intelligent skill selection and execution for diff quality analyzer workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def route_diff_analysis(diff_content: str, analysis_scope: List[str]) -> Dict: """Route diff content to appropriate quality analysis modules. Implements Law 2 (Parse at boundary) by validating diff format first. Uses multi-factor scoring to pick the best analyzer for each changed file. """ if not diff_content or not diff_content.strip(): raise ValueError("Diff content cannot be empty") parsed_diff = parse_unified_diff(diff_content) if not parsed_diff.files: return {"status": "empty", "analyzers": []} routing_plan = [] for file_path, changes in parsed_diff.files.items(): required_analyzers = [] # Language-specific routing if file_path.endswith(('.py', '.js', '.ts', '.go')): required_analyzers.append("complexity-analyzer") # Security-sensitive routing if "security" in analysis_scope or "auth" in file_path.lower(): required_analyzers.append("security-scanner") # Size-based routing if changes.added_lines > 50 or changes.removed_lines > 50: required_analyzers.append("test-coverage-checker") routing_plan.append({ "file": file_path, "analyzers": required_analyzers, "confidence": 0.95 if required_analyzers else 0.0, "change_metrics": { "added": changes.added_lines, "removed": changes.removed_lines } }) return {"routing_plan": routing_plan, "total_files": len(parsed_diff.files)} ``` ### Pattern 2: Execution with Fallback ```python def execute_analysis_pipeline(routing_plan: Dict, fallback_analyzers: Dict) -> Dict: """Execute diff quality analysis with per-analyzer fallback chains. Implements Law 4 (Fail Fast) by halting on critical security findings. Implements Law 3 (Atomic Predictability) by returning immutable result dicts. """ results = [] for route in routing_plan.get("routing_plan", []): file_results = [] for analyzer_name in route["analyzers"]: try: analyzer = get_analyzer_module(analyzer_name) score = analyzer.run(route["file"]) file_results.append({ "analyzer": analyzer_name, "score": score, "status": "passed", "latency_ms": analyzer.get_latency() }) except AnalyzerTimeoutError: # Fallback: try lightweight static check fallback = fallback_analyzers.get(analyzer_name, "basic-linter") score = get_analyzer_module(fallback).run(route["file"]) file_results.append({ "analyzer": fallback, "score": score, "status": "fallback", "latency_ms": get_analyzer_module(fallback).get_latency() }) except CriticalSecurityError: # Law 4: Fail immediately on critical issues return { "status": "blocked", "file": route["file"], "reason": "critical_vulnerability", "severity": "high" } results.append({"file": route["file"], "analysis": file_results}) return { "pipeline_status": "completed", "file_results": results, "aggregate_score": calculate_weighted_average(results), "execution_timestamp": time.time() } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Unified Diff Format RFC (RFC 2970)]() - [Git Diff Documentation]() - [SonarQube Code Quality Rules]() - [Code Review Best Practices (Google Engineering)]() - [Linting and Static Analysis Tools Comparison]() ## Related Skills | Skill | Purpose | |