Requesting Code Review

Requesting Code Review

Orchestrates intelligent skill selection and execution for requesting 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_target(
    repo_config: Dict,
    request_context: Dict,
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Select the optimal code review target and platform based on repo config.
    
    Evaluates repository metadata, branch protection rules, and historical
    reviewer availability to determine the best PR/MR target.
    
    Args:
        repo_config: Repository metadata including platform, default branch, and reviewers
        request_context: User request containing target branch, reviewers, and scope
        min_confidence: Minimum confidence threshold for target selection
        
    Returns:
        Target configuration dict with platform, branch, and reviewer list
    """
    if not repo_config or not request_context.get("target_branch"):
        raise ValueError("Repository config and target branch are required")
        
    platform = repo_config.get("platform", "github")
    default_branch = repo_config.get("default_branch", "main")
    target_branch = request_context["target_branch"]
    
    # Validate branch exists and is not protected incorrectly
    if not _validate_branch_exists(repo_config, target_branch):
        raise ValueError(f"Branch '{target_branch}' not found in repository")
        
    # Calculate match score based on branch naming conventions and reviewer load
    match_score = _calculate_branch_match_score(target_branch, default_branch)
    reviewer_load = _get_reviewer_availability(repo_config.get("reviewers", []))
    
    if match_score < min_confidence:
        return None
        
    # Return immutable target config
    return {
        "platform": platform,
        "source_branch": target_branch,
        "target_branch": default_branch,
        "reviewers": reviewer_load["available"],
        "confidence": match_score,
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_code_review_request(
    target_config: Dict,
    review_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute a code review request with platform-specific API calls and fallbacks.
    
    Creates a Pull Request or Merge Request, assigns reviewers, and attaches
    review context. Implements fallbacks for API rate limits or permission issues.
    
    Args:
        target_config: Selected target configuration from select_review_target
        review_context: PR/MR title, description, labels, and reviewer assignments
        max_retries: Maximum retry attempts for transient API failures
        
    Returns:
        Execution result with PR/MR URL, status, and reviewer confirmation
    """
    platform = target_config["platform"]
    api_client = _get_platform_client(platform)
    
    for attempt in range(max_retries + 1):
        try:
            # Create PR/MR with review context
            pr_response = api_client.create_pull_request(
                source_branch=target_config["source_branch"],
                target_branch=target_config["target_branch"],
                title=review_context["title"],
                body=review_context["description"],
                reviewers=target_config["reviewers"]
            )
            
            # Attach review metadata and labels
            api_client.add_labels(pr_response["id"], review_context.get("labels", []))
            
            return {
                "success": True,
                "platform": platform,
                "pr_url": pr_response["url"],
                "reviewers_notified": target_config["reviewers"],
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency()
            }
            
        except RateLimitError as e:
            if attempt == max_retries:
                return _fallback_to_draft_pr(target_config, review_context)
            time.sleep(2 ** attempt)
            
        except PermissionError as e:
            raise ReviewRequestError(
                f"Insufficient permissions for {platform}: {str(e)}"
            ) from e
            
    raise ReviewRequestError(
        f"Failed to create review request after {max_retries + 1} attempts"
    )

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

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 requesting, preparing for, and conducting code reviews
• Microsoft: Pull Request Etiquette — Microsoft's documentation on effective pull request practices and reviewer expectations
• GitHub: About Pull Requests — Official GitHub reference for understanding PR workflows and requesting reviews
• Effective Code Review Request Templates (Stripe) — Stripe's engineering blog on structuring review requests with context, scope, and checklists
• Code Review Culture Guide (ThoughtWorks) — ThoughtWorks Radar article on building a healthy code review culture in teams