Reflect Critique Revise

Performs a multi-pass senior engineer critique and revision of code, improving quality by catching bugs, API misuse, and style issues across domains like iOS...

The core insight: M2.7 JANGTQ-CRACK reviewing its own output in a different context catches a surprising percentage of its own mistakes. Generation context and review context activate different reasoning paths even in the same model.

The three prompts

Prompt 1 — Senior engineer critique

You are a senior {domain} engineer doing a thorough code review. You have no
investment in this code — your job is to find problems, not to validate it.

Task the author was trying to solve:
{task}

Code to review:

{draft}

{domain_specific_checklist}

For each issue you find, output:
- SEVERITY: critical | major | minor
- LOCATION: line number or function name
- ISSUE: specific problem
- FIX: concrete correction

If you find no issues, say "NO ISSUES FOUND" explicitly.

Be direct. Do not hedge. Do not explain why the code is good.

Prompt 2 — Revise based on critique

Original task:
{task}

Original code:

{draft}

Review findings:
{critique}

Produce a revised version that addresses every critical and major issue.
Minor issues: fix if clean, ignore if fix would compromise clarity.

Output ONLY the revised code. No explanation, no preamble.

Prompt 3 — Final confidence check (after final revision)

Task:
{task}

Final code:

{revised}

Rate your confidence in this code on a three-point scale:
- HIGH: you would ship this to production
- MEDIUM: works but has caveats you'd want reviewed
- LOW: has issues you can't fix without more context

Output exactly one of: HIGH, MEDIUM, LOW
Then one sentence explaining why.

Domain-specific checklists

Inject the relevant checklist into Prompt 1:

iOS checklist

Review this Swift code for:
- Deprecated API usage (any API deprecated in iOS 17+)
- Missing @MainActor annotations on UI-touching code
- Improper Task / async handling (retention cycles, missing awaits)
- SwiftUI view hierarchy issues (missing @State, @Binding, @Observable)
- SwiftData/Core Data migration safety
- Force unwraps that could crash
- Missing availability checks for iOS 26+ APIs
- Incorrect concurrency patterns (Sendable violations)

Web/frontend checklist

Review this code for:
- React rendering issues (missing keys, stale closures, effect dependencies)
- Accessibility violations (missing aria labels, keyboard navigation)
- XSS vulnerabilities (unescaped user input)
- Memory leaks (event listeners not cleaned up)
- Bundle size concerns (large imports, unused code)
- TypeScript type safety (any usage, missing types)
- Responsive / mobile breakpoint handling

Python checklist

Review this code for:
- Resource leaks (unclosed files, connections, locks)
- Exception handling gaps (bare except, swallowed errors)
- Off-by-one errors in slices/ranges
- Mutable default arguments
- Race conditions in async/threading code
- SQL injection if building queries
- Unsafe pickle/eval/exec usage
- Missing input validation

Trading checklist

Review this code for:
- Lookahead bias in backtesting (using future data)
- Survivorship bias in data selection
- Slippage/fees ignored in signal generation
- Position sizing without risk limits
- Division by zero in ratio calculations
- Missing market hours / holiday checks
- Currency/unit mixing
- Float comparison issues (use Decimal for money)

VC/analysis checklist

Review this analysis for:
- Unit confusion (ARR vs MRR, net vs gross)
- Missing risk factors (competition, moat erosion, key-person risk)
- Overly optimistic market sizing (TAM bloat)
- Unit economics fundamentals (CAC payback, LTV accuracy)
- Counterfactual reasoning (what if thesis is wrong)
- Selection bias in comparables
- Benchmark staleness

Execution logic

async def reflect_critique_revise(task, draft, domain, num_passes=2):
    current = draft
    critique_history = []

    for i in range(num_passes):
        # Pass N — critique
        critique = await llm.generate(
            prompt=PROMPT_1.format(
                task=task,
                draft=current,
                domain=domain,
                domain_specific_checklist=CHECKLISTS[domain]
            ),
            model="m27-jangtq-crack",
            system="You are a senior engineer code reviewer.",
            temperature=0.2,  # low temp for consistent critique
            max_tokens=2000
        )
        critique_history.append({"pass": i+1, "critique": critique})

        # Early exit if no issues
        if "NO ISSUES FOUND" in critique:
            break

        # Pass N — revise
        current = await llm.generate(
            prompt=PROMPT_2.format(task=task, draft=current, critique=critique),
            model="m27-jangtq-crack",
            system=f"You are a senior {domain} engineer revising code.",
            temperature=0.3,
            max_tokens=6000
        )

    # Final confidence check
    confidence_raw = await llm.generate(
        prompt=PROMPT_3.format(task=task, revised=current),
        model="m27-jangtq-crack",
        temperature=0.1,
        max_tokens=200
    )
    confidence = (
        "HIGH" if "HIGH" in confidence_raw[:10] else
        "LOW" if "LOW" in confidence_raw[:10] else
        "MEDIUM"
    )

    return {
        "code": current,
        "critique_history": critique_history,
        "confidence": confidence
    }

Cost / time

Each pass: ~2 LLM calls (critique + revise), ~5K tokens total. Default 2 passes: ~10K tokens, ~4 minutes on M4 Max at 40 t/s.

For quick tasks you can drop to num_passes=1. For critical production code, run num_passes=3 and escalate to Claude Code if confidence != HIGH.

Integration notes

This skill is called by coding-orchestrator as step 7. It can also be called standalone when user says "review this code" or pastes code with "is this right?"

When called standalone, the caller must provide domain — use route-specialist to classify if not provided.