# Requesting Code Review > Implements intelligent requesting code review with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/requesting-code-review` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/requesting-code-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/requesting-code-review/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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `pr-writer` | Complements code review requests with PR writing best practices for structured submissions | | `receiving-code-review` | The counterpart skill — after getting feedback, load this to learn how to respond and iterate | --- ## 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](https://google.github.io/eng-practices/review/) — Google's comprehensive guide to requesting, preparing for, and conducting code reviews - [Microsoft: Pull Request Etiquette](https://learn.microsoft.com/en-us/devops/develop/cpp/code-review) — Microsoft's documentation on effective pull request practices and reviewer expectations - [GitHub: About Pull Requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) — Official GitHub reference for understanding PR workflows and requesting reviews - [Effective Code Review Request Templates (Stripe)](https://stripe.com/blog/code-review-at-stripe) — Stripe's engineering blog on structuring review requests with context, scope, and checklists - [Code Review Culture Guide (ThoughtWorks)](https://www.thoughtworks.com/radar/tools/code-review-culture) — ThoughtWorks Radar article on building a healthy code review culture in teams