/01-paper-review

name: 01-paper-review
description: >
  Review academic papers for correctness, quality, and novelty using OpenJudge's
  multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip).
  Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics,
  psychology, environmental_science, mathematics, social_sciences.
  Use when the user asks to review, evaluate, critique, or assess a research paper,
  check references, or verify a BibTeX file.

Paper Review Skill

Multi-stage academic paper review using the OpenJudge `PaperReviewPipeline`:

1. **Safety check** — jailbreak detection + format validation 2. **Correctness** — objective errors (math, logic, data inconsistencies) 3. **Review** — quality, novelty, significance (score 1–6) 4. **Criticality** — severity of correctness issues 5. **BibTeX verification** — cross-checks references against CrossRef/arXiv/DBLP

Prerequisites

# Install OpenJudge
pip install py-openjudge

# Extra dependency for paper_review
pip install litellm
pip install pypdfium2  # only if using vision mode (use_vision_for_pdf=True)

Gather from user before running

| Info | Required? | Notes | |------|-----------|-------| | Paper file path | Yes | PDF or .tar.gz/.zip TeX package | | API key | Yes | Env var preferred: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc. | | Model name | No | `gpt-5.2`, `anthropic/claude-opus-4-6`, `dashscope/qwen-vl-plus`. See **Model selection** below | | Discipline | No | If not given, uses general CS/ML-oriented prompts | | Venue | No | e.g. `"NeurIPS 2025"`, `"The Lancet"` | | Instructions | No | Free-form reviewer guidance, e.g. `"Focus on experimental design"` | | Language | No | `"en"` (default) or `"zh"` for Simplified Chinese output | | BibTeX file | No | Required only for reference verification | | CrossRef email | No | Improves API rate limits for BibTeX verification |

Quick start

File type is auto-detected: `.pdf` → PDF review, `.tar.gz`/`.zip` → TeX review, `.bib` → BibTeX verification.

# Basic PDF review
python -m cookbooks.paper_review paper.pdf

# With discipline and venue
python -m cookbooks.paper_review paper.pdf \
  --discipline cs --venue "NeurIPS 2025"

# Chinese output
python -m cookbooks.paper_review paper.pdf --language zh

# Custom reviewer instructions
python -m cookbooks.paper_review paper.pdf \
  --instructions "Focus on experimental design and reproducibility"

# PDF + BibTeX verification
python -m cookbooks.paper_review paper.pdf \
  --bib references.bib --email your@email.com

# Vision mode (for models that prefer images over text extraction)
python -m cookbooks.paper_review paper.pdf \
  --vision --vision_max_pages 30 --format_vision_max_pages 10

# TeX source package
python -m cookbooks.paper_review paper_source.tar.gz \
  --discipline biology --email your@email.com

# TeX source package with Chinese output and custom instructions
python -m cookbooks.paper_review paper_source.tar.gz \
  --language zh --instructions "This is a short paper, be concise"

# Verify a standalone BibTeX file
python -m cookbooks.paper_review --bib_only references.bib --email your@email.com

All options

| Flag | Default | Description | |------|---------|-------------| | `input` (positional) | — | Path to PDF, TeX package, or .bib file | | `--bib_only` | — | Path to .bib file for standalone verification (no review) | | `--model` | `gpt-4o` | Model name | | `--api_key` | env var | API key | | `--base_url` | — | Custom API endpoint — must end at `/v1`, **not** `/v1/chat/completions` (litellm appends the path automatically) | | `--discipline` | — | Academic discipline | | `--venue` | — | Target conference/journal | | `--instructions` | — | Free-form reviewer guidance | | `--language` | `en` | Output language: `en` or `zh` | | `--bib` | — | Path to .bib file (for PDF review + reference verification) | | `--email` | — | CrossRef mailto for BibTeX check | | `--paper_name` | filename stem | Paper title in report | | `--output` | auto | Output .md report path | | `--no_safety` | off | Skip safety checks | | `--no_correctness` | off | Skip correctness check | | `--no_criticality` | off | Skip criticality verification | | `--no_bib` | off | Skip BibTeX verification | | `--vision` | **on** | Use vision mode (requires pypdfium2); enabled by default | | `--vision_max_pages` | `30` | Max pages in vision mode (0 = all) | | `--format_vision_max_pages` | `10` | Max pages for format check (0 = use `--vision_max_pages`) | | `--timeout` | `7500` | API timeout in seconds |

Interpreting results

**Review score (1–6):**

• 1–2: Reject (major flaws or well-known results)
• 3: Borderline reject
• 4: Borderline accept
• 5–6: Accept / Strong accept

**Correctness score (1–3):**

• 1: No objective errors
• 2: Minor errors (notation, arithmetic in non-critical parts)
• 3: Major errors (wrong proofs, core algorithm flaws)

**BibTeX verification:**

• `verified`: found in CrossRef/arXiv/DBLP
• `suspect`: title/author mismatch or not found — manual check recommended

Model selection

This pipeline uses [litellm](https://docs.litellm.ai/docs/providers) for model calls. Provider prefixes are handled automatically by the pipeline — see the table below.

**IMPORTANT: The model MUST support multimodal (vision) input.** PDF review uses vision mode (`--vision`) to render pages as images, which requires a vision-capable model. Text-only models will fail or produce empty reviews.

The `--model` value uses a `provider/model-name` convention so the pipeline knows which API endpoint to call. The table below shows the exact string to pass:

| Provider | `--model` value | Env var | Notes | |----------|----------------|---------|-------| | OpenAI | `gpt-5.2`, `gpt-5-mini`, … | `OPENAI_API_KEY` | No prefix needed; `gpt-5.2` is the current flagship vision model; check [OpenAI models](https://platform.openai.com/docs/models) for the latest | | Anthropic | `anthropic/claude-opus-4-6`, `anthropic/claude-