amia-code-review-patterns

name	amia-code-review-patterns
description	Use when reviewing pull requests. Trigger with PR review or code quality requests. Loaded by ai-maestro-integrator-agent-main-agent.
license	MIT
compatibility	Requires intermediate software development experience and familiarity with code review basics. Designed for reviewers analyzing pull requests with 1-30+ file changes using an 8-dimensional evaluation framework. Requires AI Maestro installed.
metadata	{"author":"Emasoft","version":"1.0.0"}
agent	ai-maestro-integrator-agent-main-agent
context	fork
user-invocable	false

Code Review Patterns Skill

Overview

Two-stage PR review: Quick Scan (70% threshold) then 8-dimension Deep Dive (80% approval).

Prerequisites

• Access to the target repository
• Python 3.8+ for helper scripts
• AI Maestro installed

Instructions

1. Gate 0 compliance - Verify requirements per requirement-compliance:
   • Table of Contents
   • 5.1 Gate 0: Requirement Compliance Overview
   • 5.2 Gate 0 Checklist Template
   • Gate 0: Requirement Compliance
   • 5.3 Review Checklist Additions
     • 5.3.1 Requirement Traceability
     • 5.3.2 Technology Compliance
     • 5.3.3 Scope Compliance
   • 5.4 Forbidden Review Approvals
   • 5.5 Correct Review Approach
2. Stage 1: Quick Scan - Assess structure, diff, issues; Go/No-Go at 70%+
3. Stage 2: Deep Dive - Score 8 dimensions; approve at 80%+
4. Run quality gates and create report via scripts/review_report_generator.py
5. Merge or reject PR; report completion via AI Maestro

Checklist

Copy this checklist and track your progress:

• Gate 0 compliance check
• Stage 1: Quick Scan (Go/No-Go at 70%+)
• Stage 2: Deep Dive (8 dimensions, approve at 80%+)
• Run quality gates (tests, linting, docs)
• Create review report via script
• Merge or reject; close issues if merged
• Report completion via AI Maestro

Output

Output Type	Format	Contents
Quick Scan Report	Markdown table	File structure, diff magnitude, issues, confidence (0-100%), Go/No-Go
Deep Dive Report	Markdown table	8-dimension scores, final confidence (0-100%), approval decision
Final Review Document	Markdown	Both stages, confidence calculations, decision rationale

Output discipline: All scripts support --output-file <path>. Use it in automated workflows to minimize token consumption.

Error Handling

Script failures return non-zero exit codes. Check stderr for details.

Examples

python scripts/review_report_generator.py --repo owner/repo --pr 42
# Output: {"stage1": "pass", "stage2": "pass", "dimensions": 8, "score": 85, "decision": "approve"}

Resources

Full reference: detailed-guide:

• Core Methodology: Two-Stage Review Process
• Key Concepts
  • Confidence Scoring System
  • Multi-Dimensional Analysis
• Quick Reference Tables
  • Confidence Score Ranges
  • Dimension Weight Summary
• Scripts Available
• AI Maestro Communication Templates
  • Template 1: Receiving PR Review Request
  • Template 2: Reporting Review Completion
  • Template 3: Requesting Clarification from Author
  • Template 4: Escalating Quality Gate Failure
• Error Handling
• Reference Document TOCs
  • Gate 0: Requirement Compliance
  • Stage One: Quick Scan
  • Stage Two: Deep Dive
  • Workflow and Decision Tree
  • Implementation Checklist
  • Review Workflow
  • Commit Conventions
• Reference Documents Index