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name: diff-quality-analyzer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent diff quality analyzer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: diff-quality-analyzer, diff quality analyzer, how do i diff-quality-analyzer, orchestrate diff-quality-analyzer, automate diff-quality-analyzer, agent diff-quality-analyzer archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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 | |