# Paper Fraud Auditor > Use when auditing a scientific paper PDF for possible image reuse, suspicious experimental data patterns, digit/Benford anomalies, table inconsistencies, fabricated-looking trends, or paper fraud risk. Converts PDFs with MinerU, builds an evidence ledger, performs strict multi-pass review, and can generate annotated evidence images for high-risk tables. - Skill: `cylqwe7855-alt/paper-fraud-auditor` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add cylqwe7855-alt/paper-fraud-auditor` - Raw SKILL.md: https://api.skillmd.com/api/skills/cylqwe7855-alt/paper-fraud-auditor/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: cylqwe7855-alt (https://skillmd.com/u/cylqwe7855-alt) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/cylqwe7855-alt/paper-fraud-auditor --- # Paper Fraud Auditor Use this skill when the user wants to审查论文 PDF、识别论文造假、检查论文图片/表格/实验数据异常、使用耿同学方法论、本福特定律、MinerU PDF 转 Markdown，或要求对论文做严格多轮审查。 ## Non-negotiables - Do not claim a paper is fraudulent from one signal. Say "异常", "疑点", "需要人工复核", or "高风险证据链". - Every finding must cite evidence location: page, figure/table number, markdown line or content block, image path, table row/column, and original value. - Benford's Law is gated by applicability. If sample size, range, or data type is unsuitable, say it is not applicable and use terminal-digit/roundness checks instead. - Treat API tokens as secrets. Never write MinerU tokens into `SKILL.md`, scripts, reports, command history, or examples. Read `MINERU_API_TOKEN` from the environment. - Prefer deterministic scripts for extraction/statistical checks, then use agents for interpretation, visual review, and adversarial explanation testing. - Evidence images must be traceable. Prefer deterministic annotated table renders from the original XLSX/PDF extraction over decorative AI images. Use generative image tools only for presentation/overview graphics, not as primary evidence. ## Required References Read only what is needed: - `references/geng-methodology.md`: image identity conflicts, data duplication, last-digit patterns, formula reverse-checks, trend/template checks, domain sanity, author-response pressure tests. - `references/benfords-law.md`: Benford formula, applicability boundaries, misuse warnings, and reporting language. - `references/mineru-api.md`: MinerU API flow and output file expectations. - `references/report-rubric.md`: evidence levels and final report template. - `references/image-backend-config.md`: optional AI image model endpoint/API configuration for presentation graphics, not primary evidence. ## Pipeline 1. **Convert PDF with MinerU** - Ask the user for a PDF path or URL if none is provided. - Set `MINERU_API_TOKEN` in the environment outside the skill files. - Run: ```bash python3 scripts/mineru_convert.py /path/to/paper.pdf --output /path/to/audit-workdir ``` - Expected output: extracted zip contents, `full.md`, `*_content_list.json`, `*_middle.json`, image files, and `mineru_manifest.json`. 2. **Build the evidence ledger** - Use MinerU outputs as source of truth. - Run: ```bash python3 scripts/build_evidence_ledger.py /path/to/audit-workdir \ --output /path/to/audit-workdir/evidence_ledger.json ``` - Expected output: `evidence_ledger.json` indexing text, tables, figures/charts/images, captions, page numbers, bounding boxes, image paths, markdown lines, content blocks, table row/column/cell values, and original values. - Preserve original values. Do not normalize away suspicious formatting. 3. **Run deterministic numeric checks** ```bash python3 scripts/numeric_forensics.py /path/to/audit-workdir --output /path/to/audit-workdir/numeric_forensics.json ``` - Default `--scope auto` prefers table numbers when enough are available, avoiding reference years/page numbers polluting the statistics. - Use this for exact duplicates, repeated decimals, terminal digits, roundness, Benford applicability, Benford deviation, and simple column relationship hints. - Treat the JSON as leads, not verdicts. 4. **Perform multi-agent audit when subagents are available** - Image Forensics Agent: same base image/different label, same subject/different signal, duplicated panels, local copy-paste. - Data Duplication Agent: repeated rows/columns, fixed offsets, repeated fractional parts, lightly modified copies. - Digit Pattern Agent: terminal 0/5 concentration, over-neat decimals, suspicious roundness. - Math Consistency Agent: percentages vs counts, reverse-engineered denominators, impossible rounding, fixed formulas. - Benford Agent: applicability first, then first-digit analysis only when allowed. - Distribution Agent: plot tables when useful, compare curve shapes and noise texture. - Domain Sanity Agent: measurement precision, biological/experimental plausibility, instrument/domain expectations. - Defense Agent: construct the strongest benign explanation for each anomaly and test whether it explains all related evidence. - Judge Agent: merge evidence, remove weak duplicates, assign risk level, and write the final report. 5. **Use vision carefully** - For each suspect image/panel, ask vision to describe concrete visual identity: subject outline, body/posture, cell/gel structure, signal layer, labels, and caption. - Do not ask vision to "decide fraud". Ask whether visual identity and experimental labels conflict. - Compare images pairwise when possible using both visual review and any available similarity/crop evidence. 6. **Generate annotated evidence images when useful** - For high-risk table or Source Data findings, render the original cells into PNG evidence cards with highlighted anomalous ranges and concise annotations. - Use this when the user asks for 生图、证据图、截图、高风险表格标注, or when the final report would benefit from visual evidence. - Default to deterministic evidence rendering; it needs no AI image API and is suitable for audit exhibits. - For Source Data audit JSON containing `fixed_difference_findings` or `terminal_findings`, run: ```bash python3 scripts/render_evidence_tables.py \ --audit-json /path/to/blind_source_audit.json \ --xlsx-root /path/to/source-data-xlsx-folder \ --output /path/to/audit-workdir/evidence_images ``` - Expected output: annotated PNG files plus `evidence_images_manifest.json`. - Each evidence image must preserve source file, sheet, range, examples, and risk note. Do not replace the written evidence ledger with images; images are a presentation layer. - If the user wants AI-generated cover images, infographics, or visual abstracts, read `references/image-backend-config.md`. Ask for or use `PAPER_AUDITOR_IMAGE_API_KEY`, `PAPER_AUDITOR_IMAGE_API_URL`, and `PAPER_AUDITOR_IMAGE_MODEL` only for that optional presentation layer. Never store keys in skill files or reports. 7. **Final report** - Start with the risk level and strongest evidence chain. - Then list findings from strongest to weakest. - For each finding include: evidence, method used, why it is suspicious, benign explanations, pressure-test result, confidence, and recommended manual verification. - Embed or link evidence images for the highest-risk findings when generated. - End with limitations: MinerU extraction quality, PDF image resolution, sample-size constraints, and whether raw data/original images are needed. ## Risk Language - **Low**: no strong anomaly; only weak or explainable signals. - **Medium**: multiple weak-to-moderate anomalies requiring human review. - **High**: independent evidence lines agree, such as repeated data plus impossible formula relation. - **Critical**: image identity conflict plus numeric generation artifacts or reverse-engineered data relationships. ## Audit Discipline - A neat number is not proof. - Benford failure is not proof. - A duplicated image can be benign only if the label, signal, caption, and author explanation remain consistent. - The strongest evidence is a network: image identity conflict, mechanical numeric pattern, reversed formula generation, and failed benign explanation.