experiment-audit

# Experiment Audit: Cross-Model Integrity Verification > 🔒 **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`](../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 **The executor collects file paths. The external reviewer backend reads code and judges integrity. The executor does NOT participate in integrity judgment.** This follows `shared-references/reviewer-independence.md` and `shared-references/experiment-integrity.md`. ## Co...