Integrity Auditor

Integrity Auditor

Overview

Paper-integrity audit package. Single stage, full quality from the start. The agent reads each reference, then carries out three evidence tracks (image / numerical / logical) and produces a structured audit_report.md with Level 1–4 graded findings.

This skill ships no LLM SDK — it is the skill instructions, references, templates, and single-purpose forensics_tools/ only.

The substantive work is decomposed into reference playbooks under references/:

Also:

• templates/audit_report.md — report skeleton the agent fills.
• forensics_tools/image_dup.py — perceptual-hash (dHash + aHash) duplicate detector for figure / panel PNGs. Single-purpose pure-Python utility (Pillow only). Catches untransformed dups.
• forensics_tools/image_dup_orb.py — ORB feature-matching duplicate detector with horizontal-flip augmentation. Catches transformed dups (rotation / flip / crop / brightness change) that perceptual hashing misses. Pair with image_dup.py: use phash first, escalate to ORB when phash distance is suspicious-but-inconclusive (16–60 range). Deps: OpenCV + NumPy.
• forensics_tools/panel_split.py — whitespace-gutter panel splitter. Pair with image_dup.py / image_dup_orb.py for cross-panel duplicate detection; whole-figure phash without panel splitting almost never finds anything.
• forensics_tools/channel_check.py — RGB channel-content classifier (DAPI / Flag / Merge / other) for fluorescence sub-images. Catches within-panel label swaps (e.g., a "DAPI" sub-image that is actually a Merge); cross-panel phash cannot catch this class.
• forensics_tools/decimal_match.py — cross-cell last-N-decimal matching sweeper for source-data XLSX. Detects fabrication where many distinct values share trailing decimal patterns (Kang Tiebang whistleblower class). Single-purpose pure-Python utility (openpyxl only). See references/02-numerical-evidence.md Check 1.5.
• forensics_tools/magnitude_consistency.py — supplement-text vs source-data XLSX unit/scale consistency. Catches unit-confusion (TWh vs GWh, mM vs µM, MHz vs Hz, etc.) and order-of-magnitude transcription errors via entity-overlap + literal-value matching across a generic SI-prefix-aware unit taxonomy covering energy / power / mass / length / area / volume / time / voltage / current / frequency / pressure / concentration / amount / force / dose / genomics-bp / CO2 / currency. Cross-family pairs (e.g., kV vs TWh) are automatically rejected. Pair with bilingual_cn_geography.json (or your own JSON map) for cross-language entity matching. Empirical baseline: Hu et al. 2026 Nature Tongyu county 1000× unit error. See references/02-numerical-evidence.md Check 1.6.
• forensics_tools/xlsx_aggregate_consistency.py — cross-XLSX same-quantity sum/row consistency. Detects when two source-data tables in the same paper purport to carry the same aggregate quantity but disagree by a small systematic margin (e.g., Hu et al. 2026 Nature MOESM3 vs MOESM6 1110.78 vs 1103.89 TWh, 0.62 percent diff, all 31 provinces same sign). Reuses the unit taxonomy from magnitude_consistency.py. Empirical baseline same paper Level 1 finding. See references/02-numerical-evidence.md Check 1.7.
• tests/smoketest.sh — < 30-second pre-commit gate. Compiles every script, runs every --help (catches argparse % bugs), and runs positive + negative controls for decimal_match, magnitude_consistency, and xlsx_aggregate_consistency. Run before every change.
• See forensics_tools/README.md for the design rule that distinguishes utility scripts from forbidden "skeleton → enrich" orchestration, and for the recommended pipeline.

Read the relevant reference before writing, not after. The full audit does not fit in a single turn — references/00-incremental-execution.md is the only execution mode that completes.

When to Use

• User hands you a paper (PDF / DOI / arXiv ID) and asks whether the figures / data / logic are trustworthy.
• User wants a quality gate on outputs from a local paper-writer run (slug-based).