# Receiving Code Review > Implements intelligent receiving code review with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/receiving-code-review` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/receiving-code-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/receiving-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/receiving-code-review --- # 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 ```python 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 ```python 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 | --- ## 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 receiving and giving code reviews - [Atlassian: Code Review Best Practices](https://www.atlassian.com/engineering/code-review-best-practices) — Atlassian's practical guidelines for effective code review workflows - [Mozilla's Guide to Code Review](https://wiki.mozilla.org/EngineeringProductivity/Guides/CodeReview) — Mozilla's engineering guide on reviewing and responding to feedback - [How to Respond to Code Reviews (Uber Engineering)](https://eng.uber.com/code-review/) — Uber's engineering blog post on handling code review feedback constructively - [Software Engineering Institute: Code Review Checklist](https://www.sei.cmu.edu/capabilities/practices/system-engineering/2021/code-review.cfm) — Carnegie Mellon SEI's structured approach to code review processes