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Multi-Agent Code Review v3.0

Perform unbiased code review using multiple specialized AI agents in fresh context. Agent count scales with story complexity. Independent perspective prevents bias. **Fresh Context, Multiple Perspectives**

Review happens in NEW session (not the agent that wrote the code)
Prevents bias from implementation decisions
Agent count determined by complexity, agents chosen by code analysis
Smart selection: touching auth code → auth-security agent, etc.

name: multi-agent-review version: 3.0.0

agent_selection: micro: {count: 2, agents: [security, code_quality]} standard: {count: 4, agents: [security, code_quality, architecture, testing]} complex: {count: 6, agents: [security, code_quality, architecture, testing, performance, domain_expert]}

available_agents: security: "Identifies vulnerabilities and security risks" code_quality: "Reviews style, maintainability, best practices" architecture: "Reviews system design, patterns, structure" testing: "Evaluates test coverage and quality" performance: "Analyzes efficiency and optimization" domain_expert: "Validates business logic and domain constraints"

<execution_context> @patterns/security-checklist.md @patterns/hospital-grade.md @patterns/agent-completion.md </execution_context>

**Select agents based on complexity**

If complexity_level == "micro":
  agents = ["security", "code_quality"]
  Display: 🔍 MICRO Review (2 agents)

Else if complexity_level == "standard":
  agents = ["security", "code_quality", "architecture", "testing"]
  Display: 📋 STANDARD Review (4 agents)

Else if complexity_level == "complex":
  agents = ALL 6 agents
  Display: 🔬 COMPLEX Review (6 agents)

**Load story file and understand requirements**

STORY_FILE="{{story_file}}"
[ -f "$STORY_FILE" ] || { echo "❌ Story file not found"; exit 1; }

Use Read tool on story file. Extract:

What was supposed to be implemented
Acceptance criteria
Tasks and subtasks
File list

**Spawn review agents in fresh context**

For each agent in selected agents, spawn Task agent:

Task({
  subagent_type: "general-purpose",
  description: "{{agent_type}} review for {{story_key}}",
  prompt: `
You are the {{AGENT_TYPE}} reviewer for story {{story_key}}.

<execution_context>
@patterns/security-checklist.md
@patterns/hospital-grade.md
</execution_context>

<context>
Story: [inline story content]
Changed files: [git diff output]
</context>

<objective>
Review from your {{agent_type}} perspective. Find issues, be thorough.
</objective>

<success_criteria>
- [ ] All relevant files reviewed
- [ ] Issues categorized by severity (CRITICAL/HIGH/MEDIUM/LOW)
- [ ] Return ## AGENT COMPLETE with findings
</success_criteria>
`
})

Wait for all agents to complete. Aggregate findings.

**Collect and categorize all findings**

Merge findings from all agents:

CRITICAL: Security vulnerabilities, data loss risks
HIGH: Production bugs, logic errors
MEDIUM: Technical debt, maintainability
LOW: Nice-to-have improvements

**Display review summary**

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🤖 MULTI-AGENT CODE REVIEW COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Agents Used: {{agent_count}}
- Security Agent
- Code Quality Agent
[...]

Findings:
- 🔴 CRITICAL: {{critical_count}}
- 🟠 HIGH: {{high_count}}
- 🟡 MEDIUM: {{medium_count}}
- 🔵 LOW: {{low_count}}

For each finding, display:

Severity and title
Agent that found it
Location (file:line)
Description and recommendation

**Suggest next steps based on findings**

📋 RECOMMENDED NEXT STEPS:

If CRITICAL findings exist:
  ⚠️ MUST FIX before proceeding
  - Address all critical security/correctness issues
  - Re-run review after fixes

If only HIGH/MEDIUM findings:
  ✅ Story may proceed
  - Consider addressing high-priority items
  - Create follow-up tasks for medium items

If only LOW/INFO findings:
  ✅ Code quality looks good
  - Optional: Address style/optimization suggestions

**When to use:** - Complex stories (≥16 tasks or high-risk keywords) - Security-sensitive code - Significant architectural changes - When single-agent review was inconclusive

When NOT to use:

Micro stories (≤3 tasks)
Standard stories with simple changes
Stories that passed adversarial review cleanly

<failure_handling> Review agent fails: Fall back to adversarial code review. API error: Log failure, continue pipeline with warning. </failure_handling>

<success_criteria>

All selected agents completed review
Findings aggregated and categorized
Report displayed with recommendations </success_criteria>

5.1 KiB Raw Blame History

Multi-Agent Code Review v3.0

5.1 KiB

Raw Blame History