/deep-review

Deep Review Command

Run the four core review axes in parallel with isolated context windows: $ARGUMENTS

/ai-eng/deep-review src/
/ai-eng/deep-review . --from-ref=main
/ai-eng/deep-review src/auth.ts src/payments.ts

What It Does

Launches 4 parallel reviews, each in its own context window:

Axis	Agent	Skill	Focus
Code Quality	code-reviewer	thermo-nuclear-code-quality-review	Maintainability, structure, 1k-line rule, spaghetti
Security	security-scanner	thermo-nuclear-security-review	Injection, auth bypass, secrets, OWASP
Architecture	architect-advisor	thermo-nuclear-architecture-review	Coupling, boundaries, layering, dependency direction
Performance	performance-engineer	thermo-nuclear-performance-review	Complexity, memory, N+1, network, bundle size

Each review runs with --depth=deep.

Process

1. Gather context — diff, changed files, PR description
2. Launch 4 parallel reviews — one per axis, each with full codebase context
3. Collect findings — each axis produces a structured report
4. Synthesize — merge into a single unified report with:
   • Cross-axis findings (e.g., security issue that also hurts performance)
   • Prioritized action items
   • Overall assessment: APPROVE, CHANGES_REQUESTED, or NEEDS_DISCUSSION

Parallel Execution by Harness

Anthropic / Claude

Use crew_agent with 4 parallel subagents:

const reviews = await Promise.all([
  crew_agent({ prompt: "Review code quality...", subagent_type: "reviewer" }),
  crew_agent({ prompt: "Review security...", subagent_type: "security-reviewer" }),
  // ...
]);

Cursor

Use Agent.create with cloud runtime and 4 parallel agents.

OpenAI / Codex

Use Runner.run() with 4 parallel Agent instances.

OpenCode

Use 4 parallel prompt sessions.

Pi

Use crew_agent with run_in_background: true for each axis.

Unified Report Format

# Deep Review Report

## Summary
- **Overall**: CHANGES_REQUESTED
- **Critical issues**: 3
- **Warnings**: 12
- **Cross-axis findings**: 2

## Code Quality
[Findings from code-reviewer with --depth=deep]

## Security
[Findings from security-scanner with --depth=deep]

## Architecture
[Findings from architect-advisor with --depth=deep]

## Performance
[Findings from performance-engineer with --depth=deep]

## Cross-Axis Findings
[Issues that span multiple axes]

## Action Items (Prioritized)
1. [P0] Fix SQL injection (Security + Code Quality)
2. [P0] Split 1,200-line file (Code Quality + Architecture)
3. [P1] Add connection pooling (Performance)
...

## Approval Bar
- No critical security vulnerabilities
- No structural regressions
- No performance degradation
- No files over 1k lines without justification

When to Use

• Before major releases — catch everything
• For critical paths — auth, payments, data pipelines
• When onboarding new team members — establish quality bar
• After large refactors — verify nothing broke across axes

When NOT to Use

• Trivial changes — use /ai-eng/review instead
• Tight deadlines — deep review takes 4x the tokens
• Established code — use targeted /ai-eng/review --type=security

$ARGUMENTS