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.

Convergence Signal (Exit Strategy) — Objective Convergence

The review cycle STOPS only when an objective bar is met: (1) the full test run has actually been executed (by you, or — in critic/subagent mode — via execution evidence supplied by the orchestrator; if neither exists, the condition is unverifiable: report the finding 'exit-bar condition unverifiable', never approve), (2) zero CRITICAL findings, (3) zero legitimate findings in logic / security / slop, and (4) only bikeshedding/style remains. That — not "I was forced to invent a flaw" — is the signal of "Maximum Viable Refinement" (Zero-Slop). Approval is bound to the objective bar; fabricating a nitpick is never the trigger to approve. Until the bar is met, keep rejecting.

3. Challenge Assumptions

• Question Everything: Do NOT accept the "happy path" as truth.
• Input Validation: What if input is null? Too long? Invalid chars?
• State: What if the DB is down? API is slow? Disk full?

4. Decision Tree

1. Is it clear? -> If not, REJECT.
2. Is it safe? -> If not, REJECT.
3. Does it break anything? -> Check regression.
4. Is it tested? -> If not, REJECT.

5. Failure Simulation

• Simulate Failures: Mentally (or physically) simulate network failures, timeouts, permission errors.
• Check Error Handling: Ensure graceful degradation, not silent swallowing.

6. Output Artifacts

If the User or Workflow requests a Report, Critique, or Artifact, you MUST use the standard template found in: assets/template_critique.md

Read this file using view_file before generating the report.

7. Rationalization Table

| Agent Excuse | Reality / Counter-Argument | | :--- | :--- | | "The code passes existing tests" | Tests only cover known scenarios. Adversarial review targets unknown unknowns. | | "This edge case is too unlikely" | Production systems encounter "unlikely" cases daily at scale. | | "I don't want to be too harsh" | Harshness is not the requirement — exhaustive reporting is. Report every issue, including low-confidence ones, with confidence + severity; filtering happens downstream. Withholding a finding to be nice is the only real failure. |

8. Examples

> [!TIP] > See examples/usage_example.md for a complete adversarial critique walkthrough.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: MatrixFounder
• Source: MatrixFounder/Universal-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.