hapo-code-review

name	hapo:code-review
description	Adversarial code review with technical rigor. Supports 3-Stage Protocol with ai-multimodal injection for multimodal spec compliance. Red-team analysis finds security holes, logic gaps, and architecture violations.
user-invocable	true
when_to_use	Invoke to review a diff for correctness and quality before merge.
category	dev-tools
keywords	["review","diff","quality","correctness"]
argument-hint	[#PR | COMMIT | --pending | scope]
metadata	{"author":"haposoft","version":"1.0.0"}

Code Review

Adversarial code review with technical rigor, evidence-based claims, and visual/document intelligence via the hapo:ai-multimodal Hub.

Runs during the hapo:develop Quality Gate (parallel to hapo:test), or standalone.

Load First

Before executing any review, firmly grasp these two pillars:

• references/spec-compliance-review.md (Stage 1 rules)
• references/verification-gate.md (Execution Proofs)
• references/pre-landing-checklists.md (Stage 2 rules)
• references/adversarial-review.md (Stage 3 rules)

Core Principles

1. YAGNI, KISS, DRY always prevail.
2. Technical correctness over social comfort. Be honest and straight to the point.
3. If Specs are provided as PDF or Design Images, do not guess — use hapo:ai-multimodal to verify.

Usage & Input Modes

If invoked without explicit arguments, default to reviewing recent changes (pending diff).

Input	Mode	Target Definition
#123	PR	Full PR diff via gh pr diff
abc1234	Commit	Single commit diff via git show
--pending	Pending	Staged + unstaged changes via git diff
scope	Path	Specific files or directories

3-Stage Adversarial Protocol

Ensure verification walks through these three stages before delivering a final score.

Stage 1 — Spec Compliance (with ai-multimodal injection)

Does the code match what was requested?

• Read contextual spec records (markdown files).
• Multimodal Delegation: If the spec references or provides PDF requirements, architecture diagrams, or UI mockups (images), STOP. Delegate to hapo:ai-multimodal scripts (e.g. gemini_batch_process.py) to parse the JSON constraints from the document, then compare the implementation against the true layout/logic constraints.
• Any missing requirements? Any unjustified extras?

Stage 2 — Code Quality

• Identify YAGNI, DRY, KISS violations.
• Ensure readability, naming conventions, and proper component boundaries (MVC/Clean Architecture compliance).
• Check for hardcoded values and missing tests.

Stage 3 — Adversarial Review (Red-Team)

Actively try to break the code.

• Edge Case Scouting: If the Pull Request modifies >= 5 files, activate hapo:inspect or call the inspector agent to scout where modified functions/components are imported and whether boundary errors exist before finishing the review.
• Find security holes (XSS, SQL Injection, Hardcoded tokens, Exposed Secrets).
• Find false assumptions, resource exhaustion loops, and race conditions.
• Find unhandled edge cases (e.g. empty strings, null pointers, negative integers).

Output Scoring

Your report MUST return a score out of 10.

• PASS: Score >= 9.5 and 0 Critical findings.
• FAIL: Score < 9.5 or > 0 Critical findings.

Format:

# Code Review Results [hapo:code-review]

**Score:** X.X / 10
**Status:** PASS | FAIL
**Target:** [PR | Commit | Path]

## Stage 1: Spec Compliance
- [Issue or OK] (If visual/PDF used, mention ai-multimodal analysis result)

## Stage 2: Code Quality
- [Issue or OK]

## Stage 3: Adversarial Findings
- [🔴 Critical] ...
- [🟡 Warning] ...
- [🔵 Suggestion] ...

## Fix Commands (Terminal ready)
[List exact bash commands or SED replacements to fix the issues quickly if possible]

Related

• Parallel skill: /hapo:test
• AI Hub: /hapo:ai-multimodal
• Parent orchestrator: quality-gate.md