Orchestration Log: When this skill is activated, append a log entry to outputs/orchestration_log.md:

### Skill Activation: Audit Engine
**Timestamp:** [current date/time]
**Actor:** AI Agent (audit-engine)
**Input:** [paper + repo being audited]
**Output:** [brief summary — e.g., "Audited 18 claims: 12 CONFIRMED, 3 PARTIAL, 2 MISSING, 1 MISMATCH"]

Audit Engine

Core Principle

Papers make claims. Code embodies what was actually done. This engine systematically checks whether the two agree. For every empirical or technical claim in the paper — datasets used, models trained, metrics reported, hyperparameters set, ablations run — the engine locates supporting evidence in the linked repository and classifies the match.

This is the complement to verification-engine, which checks citations against external sources. Audit-engine checks the paper's own claims against the paper's own code. Together they cover both failure modes of LLM-assisted writing: mis-cited prior work and unsupported own-work claims.

Inspired by the /audit command in the Feynman research agent (Companion AI, 2026), adapted to the IS/CS methodological style of this plugin.

When to Activate

• User says "audit the paper", "check paper vs. code", "verify my experiments", "reproducibility audit", "does the code match what I wrote"
• Before submitting a paper with an accompanying code release
• Before open-sourcing the repo of a published paper
• When reviewing someone else's paper + artifact
• As optional Phase 7.5 of the paper machine pipeline (after verify-citations, before prepare-submission)

When NOT to Activate

• The paper has no code artifact (pure theory, position paper, qualitative study without computational analysis) → say so and exit
• The user wants to verify citations → activate verification-engine instead
• The user wants to check writing quality → activate the writing-engine /analyze-writing command

---

Inputs

Required:

1. Paper source — paper.tex, draft.md, or explicit $ARGUMENTS path
2. Code repository — one of:
   • Local path (./experiments/, ~/repos/myproject)
   • GitHub URL (clone or use gh repo view / WebFetch on raw files)
   • Archive link (Zenodo, OSF) — ask user to download locally first

If the repo location is not supplied, scan the paper for common signals:

• "Code available at [URL]" / "Our implementation is at [URL]"
• GitHub URLs in footnotes or acknowledgements
• A code_availability section
• A REPRODUCIBILITY.md, ARTIFACT.md, or similar file sibling to the paper

If still not found: ask the user once, then exit.

---

Step 1: Extract Auditable Claims

Scan the paper for claims that can be checked against code. Ignore claims that are purely conceptual, historical, or theoretical.

Claim Categories (check in order)

Category	What to look for	Priority
Dataset	Named datasets, split sizes, sample counts, data sources	HIGH
Model	Model names, architectures, parameter counts, checkpoints	HIGH
Training	Epochs, batch size, learning rate, optimizer, hardware	HIGH
Metrics	Reported numbers (accuracy, F1, BLEU, loss values, percentages)	HIGH
Experiments	Named experimental conditions, ablations, baselines	HIGH
Hyperparameters	Specific values in tables or "Training Details"	MEDIUM
Preprocessing	Tokenization, normalization, filtering steps	MEDIUM
Evaluation	Test protocol, prompt templates, judge models, seeds	MEDIUM
Infrastructure	GPUs, training time, framework versions	LOW
Figures	Plots claimed to come from "our experiments"	MEDIUM

Extraction Pattern

For each claim, record:

{
  id: "C01",
  category: "Model",
  section: "4.2 Model Training",
  claim_text: "We fine-tune LLaMA-3-8B for 3 epochs with a learning rate of 2e-5.",
  testable_facts: [
    "model == LLaMA-3-8B",
    "epochs == 3",
    "learning_rate == 2e-5"
  ],
  priority: "HIGH"
}

Claims with concrete numbers, names, or identifiers are testable. Vague claims ("we use a standard transformer") are not auditable — mark them as NOT_AUDITABLE and skip.

Output: outputs/audit_claims.md — numbered list of all testable claims.

---

Step 2: Map the Repository

Before searching, build a lightweight mental map of the repo. Do not read every file.

1. Top-level listing — Glob on **/*.{py,ipynb,yaml,yml,json,toml,sh,md} at depth 2-3
2. Identify key files by name convention:
   • train.py, main.py, run_experiments.py, eval.py → entry points
   • config.yaml, hparams.json, sweep.yaml, *.toml → configuration
   • requirements.txt, pyproject.toml, environment.yml → dependencies
   • README.md, REPRODUCE.md, docs/ → documentation
   • results/, outputs/, logs/, wandb/ → experiment artifacts
   • datasets/, data/, load_data.py → data loaders
3. Detect framework — PyTorch, JAX, TensorFlow, HuggingFace, scikit-learn — this guides search patterns
4. Detect experiment tracking — wandb, mlflow, tensorboard, plain CSV logs

Record this as an internal map; do not output it unless the user asks.

---

Step 3: Search for Evidence (per claim)

For each testable claim, systematically search for supporting code evidence.

Search Strategy

Use Grep and Read — NOT an agent — for transparency. Each lookup should produce a file path and line number that can be cited in the report.

Example — Model claim "LLaMA-3-8B, 3 epochs, lr=2e-5":

1. Search for the model name: Grep "llama-?3-?8b|Llama-3-8B" --type py
2. Search for learning rate: Grep "2e-?5|0.00002|learning_rate.*2e-5"
3. Search for epochs: Grep "epochs\s*[:=]\s*3|num_epochs.*3"
4. Check config files: Read config/*.yaml for matching values
5. If wandb/mlflow logs exist, grep those too

Example — Metric claim "we report an F1 of 0.87":

1. Search results files: Grep "0\.87" --type json --type csv --type md
2. Search eval scripts: Grep -l "f1_score|F1" eval*.py
3. Check if the number appears in a logged output

Example — Dataset claim "trained on 12,000 examples from OpenReview":

1. Search for dataset loader: Grep "openreview" -i
2. Check size assertions: Grep "12000|12_000|len\(.*\).*12"
3. Read the data loading function to confirm source

Record Evidence

For each claim, record:

{
  id: "C01",
  searches: ["llama-3-8b", "lr=2e-5", "epochs=3"],
  hits: [
    {file: "train.py", line: 42, snippet: "model_name = 'meta-llama/Llama-3-8B'"},
    {file: "config/train.yaml", line: 7, snippet: "learning_rate: 2e-5"},
    {file: "config/train.yaml", line: 8, snippet: "epochs: 5"}  // NOTE mismatch
  ]
}

Do not hallucinate hits. If Grep returns nothing, record an empty hits list.

---

Step 4: Classify Each Claim

Classification Rubric

Status	Criteria	Evidence
CONFIRMED	Every testable fact in the claim has matching code evidence	File + line for each fact
PARTIAL	Some facts confirmed, others missing or unchecked	Confirmed facts listed; gaps called out
MISSING	No code evidence found for any fact in the claim	Which searches returned empty
MISMATCH	Code evidence exists but contradicts the claim	Side-by-side: paper says X, code says Y
NOT_AUDITABLE	Claim is too vague to check, or code is not available	Brief reason

Rules of Engagement

• Be conservative. If you're not sure a search hit actually supports the claim, mark PARTIAL and explain what's missing.
• Never mark CONFIRMED without a file:line reference. "I think it's probably in the training script" is not evidence.
• Treat MISMATCH as load-bearing. Even a single MISMATCH is worth flagging prominently — these are the findings the user most needs to know.
• Distinguish MISSING from NOT_AUDITABLE. MISSING means the claim is checkable but no evidence exists (red flag). NOT_AUDITABLE means the claim itself is too vague (usually fine, but worth rewriting).
• Do not run code. This engine is a static audit. Running experiments is the job of a separate replication step (future extension point).

---

Step 5: Generate Audit Report

Save to outputs/audit_report.md.

Report Template

# Paper-vs-Code Audit Report

**Paper:** [paper title]
**Paper source:** [paper.tex | draft.md | path]
**Code repository:** [local path or URL]
**Commit / version audited:** [git SHA if available, else "working tree"]
**Date:** [YYYY-MM-DD]
**Auditor:** audit-engine (Open Academic Paper Machine v6.4)

## Summary

| Status | Count | % |
|--------|-------|---|
| CONFIRMED | [n] | [%] |
| PARTIAL | [n] | [%] |
| MISSING | [n] | [%] |
| MISMATCH | [n] | [%] |
| NOT_AUDITABLE | [n] | [%] |

**Overall signal:** [one sentence — e.g., "Core experiments check out; 2 mismatches
in reported hyperparameters need attention before submission."]

## Critical Findings

### MISMATCH — Paper and Code Disagree

#### [C03] [Section 4.2] "We train for 3 epochs with learning rate 2e-5"
- **Paper says:** epochs = 3, lr = 2e-5
- **Code says:**
  - `config/train.yaml:8` → `epochs: 5`
  - `config/train.yaml:7` → `learning_rate: 2e-5`  ✓
- **Recommendation:** Update the paper to say 5 epochs, or re-run with 3 and
  re-check the reported numbers.

[repeat for each mismatch]

### MISSING — Claim Not Supported by Code

#### [C07] [Section 5.1] "We evaluate on the held-out 10% split (1,200 examples)"
- **Paper says:** held-out split of 1,200 examples
- **Searched:** `held.out`, `test_split`, `1200`, `0\.1`
- **Result:** No split logic found in `data/loader.py`. `eval.py` loads the entire
  dataset without stratification.
- **Recommendation:** Either add split logic to the repo or remove the claim from
  the paper.

[repeat for each missing]

## Detailed Findings by Section

### Section 4 — Method

| # | Claim | Category | Status | Evidence |
|---|-------|----------|--------|----------|
| C01 | LLaMA-3-8B model | Model | CONFIRMED | `train.py:42` |
| C02 | 2e-5 learning rate | Training | CONFIRMED | `config/train.yaml:7` |
| C03 | 3 epochs | Training | MISMATCH | see Critical Findings |
| C04 | AdamW optimizer | Training | CONFIRMED | `train.py:88` |

### Section 5 — Experiments

| # | Claim | Category | Status | Evidence |
|---|-------|----------|--------|----------|
| C05 | F1 = 0.87 on test set | Metric | CONFIRMED | `results/test_metrics.json:12` |
| C06 | 3 random seeds | Training | PARTIAL | seeds {42,43} found in `run.sh`, third seed unclear |
| C07 | Held-out 10% split | Dataset | MISSING | see Critical Findings |

[repeat for each section]

## Not Auditable

Claims that are too vague to check or depend on external state:

| # | Claim | Reason |
|---|-------|--------|
| C12 | "A standard transformer architecture" | Vague — no specific config to check |
| C15 | "Comparable to human performance" | Depends on external human benchmark |

## Repository Map (for context)

- **Entry points:** `train.py`, `eval.py`, `run_sweep.sh`
- **Configs:** `config/train.yaml`, `config/eval.yaml`
- **Data loading:** `data/loader.py`
- **Dependencies:** `requirements.txt` (53 packages)
- **Experiment tracking:** wandb (runs in `wandb/`)
- **Results:** `results/` (JSON + CSV logs)

## Methodology Note

This audit was a **static audit**: code was read and grepped, but no experiments
were re-run. Matches indicate that the paper's claims are consistent with the code
*as written*, not that the code was verified to produce the reported numbers.

A full reproducibility check would additionally:
1. Install dependencies in a clean environment
2. Re-run training with the exact config
3. Re-run evaluation and compare numbers to the paper

See Extension Points below.

## Extension Points

- **Dynamic replication:** re-run the training/eval pipeline in a Docker container
  and compare metrics to the paper (Feynman-style `/replicate`, not yet implemented
  in this plugin)
- **Diff-based re-audit:** after fixing mismatches, re-run on changed claims only
- **Multi-repo audits:** audit a paper that uses code from multiple repos (e.g.,
  baseline models from external sources)

---

Prioritization Strategy

If the paper has many claims, process in this order and save intermediate results after each tier so the user can act early:

Tier 1 — High-stakes claims (verify first)

• Reported numbers in tables and abstract
• Model identifier and size
• Dataset identifier and size
• Any claim the paper's contribution depends on

Tier 2 — Method details

• Hyperparameters, optimizers, schedulers
• Preprocessing, tokenization
• Evaluation protocol

Tier 3 — Context

• Infrastructure (GPUs, training time)
• Framework versions
• Figure provenance

After Tier 1, present a progress snapshot:

AUDIT PROGRESS
Tier 1 (High-stakes):  [12/12] Complete
  CONFIRMED: 9 | PARTIAL: 1 | MISSING: 1 | MISMATCH: 1
  >> 1 mismatch found — see audit_report.md

Tier 2 (Method):       [0/8]  Queued
Tier 3 (Context):      [0/4]  Queued

Continue to Tier 2? [proceeding unless you redirect]

---

Limitations

What This Engine Can Do

• Check named entities (datasets, models) against code references
• Verify hyperparameter values in configs match the paper
• Confirm metrics appear in logged results
• Catch contradictions between paper and code (MISMATCH)
• Catch unsupported claims (MISSING)
• Flag vague claims that should be rewritten (NOT_AUDITABLE)

What This Engine Cannot Do

• Run the code and reproduce the numbers (static audit only)
• Detect whether the code would actually produce the claimed behaviour if run
• Audit closed-source or paywalled dependencies
• Audit manual/qualitative steps ("we prompted GPT-4 iteratively until...")
• Replace a human artifact reviewer

Failure Modes to Watch For

• Config vs. code divergence — the YAML says one thing, train.py hard-codes another. Always check both.
• Scripts that override configs — run.sh may pass --epochs 10 that overrides the YAML. Grep shell scripts too.
• Multiple configs — papers often report one experiment; the repo has ten. Match the config to the paper, not the other way around.
• Notebooks — .ipynb files are JSON; Grep works but read carefully.

---

Relationship to Other Skills

• verification-engine — checks external citations against sources. audit-engine checks own-work claims against own code. Run both before submission for full coverage.
• review-engine — simulated peer review. An audit report is a natural input to a simulated reviewer.
• prepare-submission — a clean audit report is a strong artifact to include with submissions to venues that accept reproducibility statements (e.g. NeurIPS Reproducibility Checklist, ML Reproducibility Challenge).