Experiment Audit: Cross-Model Integrity Verification

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. It is verdict-bearing, it judges experiment integrity. Re-running that verdict on a timer adds no new signal, and a loop that accepts its own output to decide when to stop crosses into self-acquittal (acceptance-gate.md). Schedule the external wait that precedes it, experiments done → then audit once. See shared-references/external-cadence.md.

Audit experiment integrity for: $ARGUMENTS

Why This Exists

LLM agents can produce fraudulent experimental results through:

1. Fake ground truth, creating synthetic "reference" from model outputs, then reporting high agreement as performance
2. Score normalization, dividing metrics by the model's own max to get 0.99+
3. Phantom results, claiming numbers from files that don't exist or functions never called
4. Insufficient scope, reporting 2-scene pilots as "comprehensive evaluation"

These are NOT intentional deception, they are failure modes of optimizing agents that lack integrity constraints. This skill adds that constraint.

Core Principle