code-review

Code Review (Adversarial + Receiving)

Reference Files

• references/review-order-and-checkpoints.md — review order, checkpoint discipline
• references/code-review-heuristics.md — heuristics, pattern recognition, false-positive prevention
• references/security-review-checklist.md — security review checklist

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Mode: ADVERSARIAL REVIEW

Only report issues with confidence ≥80. Every finding states category, impact, and why it matters. Present a recommendation, not a menu. Be opinionated.

Signal quality rule: One finding with file:line evidence and a fix is worth more than ten generic observations. Never report a pattern without showing where it lives.

Two-Stage Review

Stage 1: Spec Compliance — Does the code do what the plan/spec asked? Check: phase exit criteria met, interfaces match plan's Consumes/Produces, no scope drift, no missing scenarios.

Stage 2: Code Quality — Is the code well-built? Check: correctness, performance, security, clarity, test coverage.

Review Order

Top-down (spec → architecture → module → function → line) for first pass. Bottom-up (line → function → module) for detail pass. See references/review-order-and-checkpoints.md.

Severity Classification

Severity	Criteria
CRITICAL	Data loss, security breach, silent data corruption
HIGH	User-visible broken behavior
MEDIUM	Suboptimal but functional
LOW	Code smell, style

Confidence Scoring

Confidence	Meaning
90-100	Verified: read the code, confirmed the issue, can cite file:line
80-89	Strong: read surrounding context, pattern is clear
<80	Do not report — insufficient evidence

Parallel Review + Router Merge

When code-reviewer and failure-hunter run in parallel (BUILD workflow):

• code-reviewer (Assessment A): correctness, performance, spec compliance. Forms opinion WITHOUT seeing the hunter's scan.
• failure-hunter (Assessment B): silent failure scan using red-flags table. Does NOT see the reviewer's findings.
• Router-owned merge: after both complete, the router writes a merged findings summary into the workflow artifact before verifier handoff. Where both agree → high confidence. Where the hunter caught what the reviewer missed → keep. Where the hunter finding is a false positive → drop with reason. Contradictory verdicts: stricter verdict wins, logged in status_history.

Zero-Finding Halt

Zero findings on a non-trivial change → insufficient depth, not perfect code. Re-scan against heuristics and security checklist before reporting CLEAN.

Code Smells (Fowler Catalog)

Scan for these 12 named smells during review. Each is actionable — not a style preference. "Messy" is not actionable; "Mysterious Name" is:

Smell	Signal	Fix
Mysterious Name	Function/variable name doesn't reveal intent	Rename to describe what it does
Long Method	Method > 20 lines doing multiple things	Extract sub-methods
Long Parameter List	> 4 parameters — consider parameter object	Extract into an object
Large Class	Class with too many responsibilities	Split by responsibility
Data Class	Holds data, no behavior — anemic domain model	Move behavior in, or inline the class
Duplicated Code	Same logic in 3+ places	Extract shared function
Feature Envy	Method reads more from another class than its own	Move method to the class it envies
Shotgun Surgery	One change requires touching many files	Consolidate responsibility
Divergent Change	One class changes for different reasons	Split into separate classes
Primitive Obsession	Using primitives where a small value object would add meaning	Create a value object
Repeated Switches	Same switch/if-else on a type across files	Replace with polymorphism
Speculative Generality	Abstraction for future use that never comes	Delete it (YAGNI)
Message Chains	a.b().c().d() — client knows the object graph	Hide the chain behind a method
Middle Man	Class just delegates to another — adds no logic	Remove the middleman, use the real object
Refused Bequest	Subclass doesn't use parent's methods	Replace inheritance with composition
Data Clumps	3+ values always passed together	Extract into an object

Repo standards override baseline: if the repo's documented conventions endorse something this baseline would flag, suppress the smell.

AI-Generated Anti-Patterns

Patterns commonly produced by AI code generation — flag with elevated priority:

• Over-eager memoization — useMemo/useCallback/React.memo wrapping everything without profiling evidence
• State duplication — same state stored in 2+ places, kept in sync manually (source of truth unclear)
• Sequential awaits — independent async calls awaited sequentially instead of Promise.all (latency multiplier)
• Over-fetching — fetching full objects (or more than the current view needs) when only one field is needed
• Premature abstraction / speculative generality — interface with single implementation "for future flexibility"
• Configurable when it should be constant — adding options/flags for flexibility nobody asked for
• Defensive coding for impossible states — null checks / over-engineered error handling for scenarios that can't happen (values typed non-nullable, internal code boundaries)
• Test mirrors implementation — test recomputes expected value using the same logic as the code (tautological test)
• Factory overkill — factory pattern for objects with no polymorphism
• Type assertions instead of type guards — as any/as Type instead of narrowing with a runtime check

Metric Honesty Rule

Never fabricate metrics. An LLM reading static source code cannot measure real-world LCP, INP, CLS, memory usage, or runtime performance.

• Can assess from code: algorithmic complexity (O(n) vs O(n²)), N+1 query patterns, obvious hot loops, missing indices
• Cannot assess from code: real-world latency, actual memory pressure, real INP/LCP/CLS values

State what you CAN verify from code. Tag anything else as "potential impact, not measured" — never invent numbers. Recommend specific tools (Lighthouse, profiler, benchmark suite) when runtime measurement is the real answer.

Deferred Findings (Not "Residual" — Already Wired)

Minor/Medium findings you don't fix in this pass are NOT dropped, but you do NOT need a separate file or a separate CONTRACT field for them. Just report them normally with severity and file:line in your output. The router already handles persistence: it reads your findings, appends every non-blocking Minor item to the workflow artifact's deferred_findings array (source, phase, finding, severity), and surfaces the accumulated list for explicit user triage at BUILD-DONE finishing. Nothing is silently discarded — this is automatic on the router side, not something you need to engineer in your response.

False Positive Prevention

Do NOT flag:

• Code that matches an explicit project convention (check patterns.md)
• Intentional simplification documented in the plan
• Test-only code using test patterns (mocks, fixtures, stubs)
• Performance "issues" without a measured bottleneck
• Style preferences that don't match project conventions

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Mode: RECEIVING REVIEW

Discipline for acting on external/human review feedback (pasted PR comments, review notes, "can you change X"). This governs the MAIN session, not the internal reviewer→router→fix loop.

The 6-Step Loop

1. Read all feedback before responding to any item
2. Categorize each item: CRITICAL (must fix), IMPORTANT (should fix), MINOR (optional), REJECT (with reason)
3. Verify before agreeing — don't blindly accept. Check if the feedback is correct against the code.
4. Fix accepted items — CRITICAL first, then IMPORTANT
5. Push back on rejected items — with evidence, not opinion
6. Report — what was fixed, what was rejected and why

Verify Before Agreeing

Before implementing a suggestion, check:

• Does the issue actually exist in the code? (read the file:line)
• Is the suggested fix correct? (would it actually fix the issue?)
• Does the fix introduce new problems? (side effects, breaking changes)
• Is the suggestion based on a correct understanding of the code?

YAGNI-Grep Before Implementing

Before implementing a suggestion, grep the codebase for the pattern the reviewer claims is wrong. If the pattern is project convention (appears in many places, is in patterns.md), push back. If it's genuinely isolated, fix it.

When To Push Back

Situation	Response
Reviewer misunderstood the code	Explain with file:line evidence
Suggestion contradicts project convention	Cite the convention, push back
Suggestion adds unnecessary complexity	YAGNI — state why the simpler approach is better
Suggestion is correct but out of scope	Acknowledge, defer to a follow-up
Suggestion is a style preference	Acknowledge, apply only if it matches project conventions

Precedence

Pushing back ≠ refusing. You must either fix the issue or provide evidence why it's not an issue. "I prefer my way" is not a valid push-back. "This is project convention, see patterns.md line X" is valid.

Note

The Fowler Code Smells catalog, AI-Generated Anti-Patterns, Metric Honesty Rule, and Deferred Findings handling are defined once above under Mode: ADVERSARIAL REVIEW. There is no second copy — apply those sections during both the adversarial pass and any receiving-review triage.