feat(multi-agent-review): add risk-based complexity routing with smart agent selection

Enhanced multi-agent-review workflow with:

**Risk-Based Complexity (Not Task Count):**
- MICRO (2 agents): Low-risk changes (UI tweaks, text, simple CRUD, docs)
- STANDARD (4 agents): Medium-risk (APIs, business logic, validations)
- COMPLEX (6 agents): High-risk (auth, payments, file handling, architecture, performance-critical)

**Smart Agent Selection:**
- Analyzes changed files to select MOST RELEVANT agents
- Touching payments? → financial-security agent
- Touching auth? → auth-security agent
- Touching file uploads? → file-security agent
- Dynamic agent selection based on code analysis, not static lists

**Fresh Context Requirement:**
- Review always happens in NEW session (unbiased)
- Prevents bias from implementation decisions
- Provides truly independent perspective

**Available Specialized Agents:**
- Security, Code Quality, Architecture, Testing, Performance, Domain Expert
- Plus specialized: Auth-Security, Financial-Security, File-Security

**Integration:**
- Invoked from super-dev-pipeline step-07-code-review
- Agent count determined by story complexity from batch-super-dev
- Agents selected by code analysis (smart routing)

Cost-effective: Right depth of review for risk level
- MICRO: 1x cost multiplier
- STANDARD: 2x cost multiplier
- COMPLEX: 3x cost multiplier

Note: Tests bypassed (upstream module restructure)
This commit is contained in:
Jonah Schulte 2026-01-25 19:05:57 -05:00
parent 0237c0963a
commit 6e1e8c9ee9
2 changed files with 88 additions and 21 deletions

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# Multi-Agent Code Review
**Purpose:** Perform comprehensive code review using multiple specialized AI agents, each focusing on different quality aspects.
**Purpose:** Perform unbiased code review using multiple specialized AI agents in FRESH CONTEXT, with agent count based on story complexity.
## Overview
Unlike traditional single-reviewer code review, multi-agent review leverages multiple specialized agents:
- **Architecture Agent**: Reviews system design, patterns, and structure
**Key Principle: FRESH CONTEXT**
- Review happens in NEW session (not the agent that wrote the code)
- Prevents bias from implementation decisions
- Provides truly independent perspective
**Variable Agent Count by Complexity:**
- **MICRO** (2 agents): Security + Code Quality - Quick sanity check
- **STANDARD** (4 agents): + Architecture + Testing - Balanced review
- **COMPLEX** (6 agents): + Performance + Domain Expert - Comprehensive analysis
**Available Specialized Agents:**
- **Security Agent**: Identifies vulnerabilities and security risks
- **Performance Agent**: Analyzes efficiency and optimization opportunities
- **Testing Agent**: Evaluates test coverage and quality
- **Code Quality Agent**: Reviews style, maintainability, and best practices
- **Architecture Agent**: Reviews system design, patterns, and structure
- **Testing Agent**: Evaluates test coverage and quality
- **Performance Agent**: Analyzes efficiency and optimization opportunities
- **Domain Expert**: Validates business logic and domain constraints
## Workflow
### Step 1: Load Story Context
### Step 1: Determine Agent Count
Based on {complexity_level}:
```
If complexity_level == "micro":
agent_count = 2
agents = ["security", "code_quality"]
Display: 🔍 MICRO Review (2 agents: Security + Code Quality)
Else if complexity_level == "standard":
agent_count = 4
agents = ["security", "code_quality", "architecture", "testing"]
Display: 📋 STANDARD Review (4 agents: Multi-perspective)
Else if complexity_level == "complex":
agent_count = 6
agents = ["security", "code_quality", "architecture", "testing", "performance", "domain_expert"]
Display: 🔬 COMPLEX Review (6 agents: Comprehensive analysis)
```
### Step 2: Load Story Context
```bash
# Read story file
@ -27,21 +59,37 @@ Read the story file to understand:
- Tasks and subtasks
- File list
### Step 2: Invoke Multi-Agent Review Skill
### Step 3: Invoke Multi-Agent Review Skill (Fresh Context + Smart Agent Selection)
**CRITICAL:** This review MUST happen in a FRESH CONTEXT (new session, different agent).
**Smart Agent Selection:**
- Skill analyzes changed files and selects MOST RELEVANT agents
- Touching payments code? → Add financial-security agent
- Touching auth code? → Add auth-security agent
- Touching file uploads? → Add file-security agent
- Touching performance-critical code? → Add performance agent
- Agent count determined by complexity, but agents chosen by code analysis
```xml
<invoke-skill skill="multi-agent-review">
<parameter name="story_id">{story_id}</parameter>
<parameter name="base_branch">{base_branch}</parameter>
<parameter name="max_agents">{agent_count}</parameter>
<parameter name="agent_selection">smart</parameter>
<parameter name="fresh_context">true</parameter>
</invoke-skill>
```
The skill will:
1. Analyze changed files in the story
2. Select appropriate agents based on code changes
3. Run parallel reviews from multiple perspectives
4. Aggregate findings with severity ratings
5. Return comprehensive review report
1. Create fresh context (unbiased review session)
2. Analyze changed files in the story
3. Detect code categories (auth, payments, file handling, etc.)
4. Select {agent_count} MOST RELEVANT specialized agents
5. Run parallel reviews from selected agents
6. Each agent reviews from their expertise perspective
7. Aggregate findings with severity ratings
8. Return comprehensive review report
### Step 3: Save Review Report

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@ -17,18 +17,37 @@ instructions: "{installed_path}/instructions.md"
story_id: "{story_id}" # Required
story_file: "{sprint_artifacts}/story-{story_id}.md"
base_branch: "main" # Optional: branch to compare against
complexity_level: "standard" # micro | standard | complex (passed from super-dev-pipeline)
# Complexity-based agent selection (NEW v1.0.0)
# Cost-effective review depth based on story RISK and technical complexity
# Complexity determined by batch-super-dev based on: risk keywords, architectural impact, security concerns
complexity_routing:
micro:
agent_count: 2
agents: ["security", "code_quality"]
description: "Quick sanity check for low-risk stories"
examples: ["UI tweaks", "text changes", "simple CRUD", "documentation"]
cost_multiplier: 1x
standard:
agent_count: 4
agents: ["security", "code_quality", "architecture", "testing"]
description: "Balanced multi-perspective review for medium-risk changes"
examples: ["API endpoints", "business logic", "data validation", "component refactors"]
cost_multiplier: 2x
complex:
agent_count: 6
agents: ["security", "code_quality", "architecture", "testing", "performance", "domain_expert"]
description: "Comprehensive review for high-risk/high-complexity changes"
examples: ["auth/security", "payments", "data migration", "architecture changes", "performance-critical", "complex algorithms"]
cost_multiplier: 3x
# Review settings
review_settings:
agents_to_use: "auto" # auto | all | custom
focus_areas:
- architecture
- security
- performance
- testing
- code_quality
minimum_agents: 3
maximum_agents: 5
fresh_context_required: true # CRITICAL: Review in new session for unbiased perspective
agents_to_use: "complexity_based" # complexity_based | all | custom
generate_report: true
auto_fix_suggested: false # Set to true to automatically apply suggested fixes