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name: receiving-code-review compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent receiving code review 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: receiving-code-review, receiving code review, how do i receiving-code-review, orchestrate receiving-code-review, automate receiving-code-review, agent receiving-code-review 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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Receiving Code Review

Orchestrates intelligent skill selection and execution for receiving code review 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 select_review_skill(
    pr_context: Dict[str, Any],
    available_review_skills: List[Dict],
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Select optimal code review skill based on PR metadata and diff analysis.
    
    Evaluates language, security flags, and complexity to route to:
    - security-audit (for auth/crypto changes)
    - style-linter (for formatting/CI failures)
    - architecture-review (for large refactors)
    
    Args:
        pr_context: Dictionary containing PR URL, diff, and metadata
        available_review_skills: List of review skill metadata
        min_confidence: Minimum routing confidence threshold
        
    Returns:
        Selected review skill with routing metadata or None
    """
    if not pr_context.get("diff"):
        raise ValueError("PR diff is required for review routing")
        
    diff_text = pr_context["diff"]
    detected_lang = _detect_language_from_diff(diff_text)
    has_security_keywords = _scan_for_security_patterns(diff_text)
    complexity = _estimate_complexity(diff_text)
    
    best_match = None
    best_score = 0.0
    
    for skill in available_review_skills:
        score = 0.0
        if skill["name"] == "security-audit" and has_security_keywords:
            score += 0.6
        elif skill["name"] == "style-linter" and complexity < 50:
            score += 0.5
        elif skill["name"] == "architecture-review" and complexity >= 50:
            score += 0.7
            
        if skill.get("supported_languages") and detected_lang not in skill["supported_languages"]:
            score *= 0.2
            
        if score > best_score and score >= min_confidence:
            best_score = score
            best_match = skill
            
    if best_match:
        return {**best_match, "routing_confidence": best_score, "detected_language": detected_lang}
    return None

Pattern 2: Execution with Fallback

def execute_review_with_fallback(
    selected_skill: Dict,
    pr_context: Dict,
    fallback_chain: List[str] = None
) -> Dict:
    """Execute code review with domain-specific fallback routing.
    
    Implements Fail Fast, Fail Loud for review pipelines:
    - Invalid diffs halt immediately
    - Security bypasses escalate without retry
    - Tool timeouts cascade to lighter analyzers
    
    Fallback chain:
    1. Retry with stricter linting rules
    2. Fall back to generic linter if specialized tool fails
    3. Escalate to human senior engineer if security flags remain unresolved
    
    Args:
        selected_skill: Previously routed review skill metadata
        pr_context: Original PR context and diff
        fallback_chain: Ordered list of fallback skill names
        
    Returns:
        Review result with findings, status, and routing metadata
    """
    if fallback_chain is None:
        fallback_chain = ["generic-linter", "human-escalation"]
        
    attempt = 0
    max_attempts = 2
    
    while attempt <= max_attempts:
        try:
            result = _run_review_tool(selected_skill["name"], pr_context)
            return {
                "status": "completed",
                "skill": selected_skill["name"],
                "findings": result.get("issues", []),
                "attempts": attempt + 1,
                "latency_ms": _measure_execution_time()
            }
        except ToolTimeoutError:
            attempt += 1
            if attempt > max_attempts:
                next_skill_name = fallback_chain.pop(0) if fallback_chain else "human-escalation"
                selected_skill["name"] = next_skill_name
                attempt = 0
            continue
        except CriticalSecurityBypassError as e:
            return {
                "status": "escalated",
                "skill": "human-escalation",
                "reason": f"Security bypass detected: {e}",
                "priority": "high"
            }
            
    return {"status": "failed", "reason": "All review attempts exhausted"}

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

Related Skills

Skill	Purpose
requesting-code-review	The counterpart skill — use this to learn how to structure reviews so they receive good feedback
code-review	Provides the review methodology that both reviewers and authors should follow

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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 domain. The model follows markdown links at load time to resolve external references and inline content.

• Google Engineering Practices: Code Review — Google's comprehensive guide to receiving and giving code reviews
• Atlassian: Code Review Best Practices — Atlassian's practical guidelines for effective code review workflows
• Mozilla's Guide to Code Review — Mozilla's engineering guide on reviewing and responding to feedback
• How to Respond to Code Reviews (Uber Engineering) — Uber's engineering blog post on handling code review feedback constructively
• Software Engineering Institute: Code Review Checklist — Carnegie Mellon SEI's structured approach to code review processes