/experiment-audit

name: experiment-audit
description: "Audit experiment integrity before claiming results. Uses cross-model review (external reviewer backend) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says \"审计实验\", \"check experiment integrity\", \"audit results\", \"实验诚实度\", or after experiments complete before writing claims."
argument-hint: "[experiment-dir-or-results-path]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply, mcp__manual_review__review, mcp__manual_review__review_reply

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`.

Constants

• **REVIEWER_BACKEND = `codex`** — Default: Codex MCP (ultra). Override with `— reviewer: oracle-pro` for Oracle MCP, or `— reviewer: manual` for Manual Review MCP. If manual-review MCP is unavailable, stop and print the install command; do not fall back to Codex. See `shared-references/reviewer-routing.md`.

Reviewer Calling Convention

When calling the reviewer, branch on REVIEWER_BACKEND:

**If REVIEWER_BACKEND = `codex`:** Use `mcp__codex__codex` for new review threads. Use `mcp__codex__codex-reply` for follow-up rounds (reuse threadId).

**If REVIEWER_BACKEND = `manual`:** Use `mcp__manual_review__review` for new review threads with: prompt: [exact same prompt that would go to Codex] config: {"model_reasoning_effort": "xhigh"} Save the returned `threadId`. Use `mcp__manual_review__review_reply` for follow-up rounds with: threadId: [saved manual-review threadId] prompt: [follow-up prompt] config: {"model_reasoning_effort": "xhigh"}

Prompt fidelity: the manual prompt must be exactly the same text that Codex would receive. Review tracing applies equally to both backends.

Workflow

Step 1: Collect Artifacts (Executor — Claude)

Locate and list these files WITHOUT reading or summarizing their content:

Scan project directory for:
1. Evaluation scripts:    *eval*.py, *metric*.py, *test*.py, *benchmark*.py
2. Result files:          *.json, *.csv in results/, outputs/, logs/
3. Ground truth paths:    look in eval scripts for data loading (dataset paths, GT references)
4. Experiment tracker:    EXPERIMENT_TRACKER.md, EXPERIMENT_LOG.md
5. Paper claims:          NARRATIVE_REPORT.md, paper/sections/*.tex, PAPER_PLAN.md
6. Config files:          *.yaml, *.toml, *.json configs with metric definitions

**DO NOT summarize, interpret, or explain any file content.** Only collect paths.

Step 2: Send to Reviewer

Based on the selected reviewer backend (see Reviewer Calling Convention), pass ONLY file paths and the audit checklist to the reviewer. The reviewer reads everything directly.

For `codex`, call `mcp__codex__codex` with:

• `model: gpt-5.6-sol`
• `config: {"model_reasoning_effort": "ultra"}`
• `sandbox: read-only`
• `cwd: [project directory]`
• `prompt: [the exact full prompt below]`

For `manual`, call `mcp__manual_review__review` with:

• `config: {"model_reasoning_effort": "xhigh"}`
• `prompt: [the exact full prompt below]`

Manual review cannot use Codex-only `model`, `sandbox`, or `cwd`; include the same file paths in the prompt so the user can inspect them.

Use this exact prompt for both backends:

You are an experiment integrity auditor. Start from the assumption that the
    evaluation is compromised somewhere — your job is to find where. Be
    adversarial. Trust nothing the author tells you — verify everything
    yourself. Read ALL files listed below and check for the following fraud
    patterns.

    Files to read:
    - Evaluation scripts: [list paths]
    - Result files: [list paths]
    - Experiment tracker: [list paths]
    - Paper claims: [list paths]
    - Config files: [list paths]

    ## Audit Checklist

    ### A. Ground Truth Provenance
    For each evaluation script:
    1. Where does "ground truth" / "reference" / "target" come from?
    2. Is it loaded from the DATASET, or generated/derived from MODEL OUTPUTS?
    3. If derived: is it explicitly labeled as proxy evaluation?
    4. Are official eval scripts used when available for this benchmark?
    FAIL if: GT is derived from model outputs without explicit proxy labeling.

    ### B. Score Normalization
    For each metric computation:
    1. Is any metric divided by max/min/mean of the model's OWN output?
    2. Are raw scores reported alongside any normalized scores?
    3. Are any scores suspiciously close to 1.0 or 100%?
    FAIL if: Normalization denominator comes from prediction s