vdd-adversarial

VDD Adversarial

1. Red Flags (Anti-Rationalization)

STOP and READ THIS if you are thinking:

• "The code passes tests, so it's fine" -> WRONG. Tests only cover what the author imagined. You MUST find what they missed.
• "This edge case is unlikely" -> WRONG. Unlikely ≠ impossible. If it crashes, it WILL crash in production.
• "The happy path works, that's enough" -> WRONG. Adversarial review exists to destroy happy-path assumptions.
• "I'll skip the template, it's just a quick review" -> WRONG. Every critique MUST use assets/template_critique.md.

2. VDD Methodology Context

This skill implements the Iterative Adversarial Refinement phase ("The Roast") from the VDD methodology.

Your Role: You are the Adversary. The Builder has already passed the Verification Loop (tests + HITL). Your job is to find what survived that phase.

Key Principles (see references/vdd-methodology.md for full methodology):

• Anti-Slop Bias: The first "correct" version is the most dangerous — hidden technical debt lurks beneath.
• Exhaustive Reporting (supersedes "Forced Negativity"): report every issue, including low-confidence ones, with confidence + severity attached — filtering happens downstream, never in the reviewer's head. Zero tolerance for "lazy" AI patterns (placeholder comments, generic error handling, inefficient loops).
• Context Resetting: Each adversarial review MUST use a fresh context window. Why (documented mechanisms, audit-067 C-02): multi-turn assumption lock-in — models lock onto early assumptions and degrade ~39% vs single-turn on the same tasks (arXiv:2505.06120); context rot — accumulated history dilutes attention as context grows (Chroma 2025); pushback-driven sycophantic belief updates within a session (TRUTH DECAY / SYCON-Bench). A fresh window restores single-turn rigor.
• Linear Accountability: Every line of code MUST trace to a corresponding issue and verification step.

Empirical positioning (ab-experiment-075, pre-registered rule 3): this skill is a precision tool, not a recall lever. Against a plain exhaustive baseline ("report everything with confidence + severity") the adversarial scaffolding scored −6.9pp recall but −16% false positives and a 3.9% vs 13.0% bikeshedding ratio (N=3, 24 sealed seeded bugs — docs/reviews/ab-experiment-075.md). Load it when noise/FP cost dominates (triage queues, high-volume review); for recall-critical passes prefer the plain exhaustive prompt, or /vdd-multi when class-complete coverage justifies 3× cost.