self-review

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Automatically review an academic paper using the NeurIPS review form with three reviewer personas, ensemble scoring, and reflection refinement. Extracts text from PDF, runs structured review, and outputs actionable feedback. Use when the user wants to review a paper before submission or get feedback on a draft.

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Sourcelingzhi227/claude-skills

Added on2026-02-20

NPX Install

npx skill4agent add lingzhi227/claude-skills self-review

SKILL.md Content

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Self-Review

Review an academic paper using a structured review form with multiple reviewer personas.

Input

```
$ARGUMENTS
```
— Path to PDF file or
```
.tex
```
file

Scripts

Extract text from PDF

bash

python ~/.claude/skills/self-review/scripts/extract_pdf_text.py paper.pdf --output paper_text.txt
python ~/.claude/skills/self-review/scripts/extract_pdf_text.py paper.pdf --format markdown

Tries pymupdf4llm (best) → pymupdf → pypdf. Install:

pip install pymupdf4llm pymupdf pypdf

Parse PDF into structured sections

bash

python ~/.claude/skills/self-review/scripts/parse_pdf_sections.py \
  --pdf paper.pdf --output sections.json

Extracts title (via font size), section headings, and section text. Requires:

pip install pymupdf

Key flags:

--format text

,

--verbose

Workflow

Step 1: Load Paper

If PDF: use
```
extract_pdf_text.py
```
to extract text
If
```
.tex
```
: read the LaTeX source directly

Step 2: Three-Persona Review

Run three independent reviews using different personas (from

references/review-form.md

):

Harsh but fair reviewer: Expects good experiments that lead to insights
Harsh and critical reviewer: Looking for impactful ideas in the field
Open-minded reviewer: Looking for novel ideas not proposed before

For each persona, generate a review following the NeurIPS review JSON format in

references/review-form.md

.

Step 3: Reflection Refinement (up to 3 rounds per reviewer)

After each review, apply the reflection prompt: re-evaluate accuracy and soundness, refine if needed. Stop when "I am done".

Step 4: Aggregate

Combine all three reviews
Average numerical scores (round to nearest integer)
Synthesize a meta-review finding consensus
Weight scores using AgentLaboratory weights: Overall (1.0), Contribution (0.4), Presentation (0.2), others (0.1 each)

Step 5: Actionable Report

Output format:

## Review Summary
- **Overall Score**: X/10 (Weighted: Y/10)
- **Decision**: Accept / Reject
- **Confidence**: Z/5

## Strengths (consensus across reviewers)
1. ...
2. ...

## Weaknesses (consensus across reviewers)
1. ...
2. ...

## Questions for Authors
1. ...

## Specific Suggestions for Improvement
1. [Section X, Page Y]: ...
2. [Section Z, Page W]: ...

## Score Breakdown
| Dimension | R1 | R2 | R3 | Avg |
|-----------|----|----|-----|-----|
| Overall | ... | ... | ... | ... |
| Contribution | ... | ... | ... | ... |
| ... | ... | ... | ... | ... |

References

NeurIPS review form, scoring weights, personas, reflection prompts:
```
~/.claude/skills/self-review/references/review-form.md
```

PDF text extraction:

~/.claude/skills/self-review/scripts/extract_pdf_text.py

Missing Sections Check

You MUST verify that all required sections are present: Abstract, Introduction, Methods/Approach, Experiments/Results, Discussion/Conclusion. Reduce scores if any are missing.

Related Skills

Upstream: paper-compilation
Downstream: paper-revision, rebuttal-writing
See also: slide-generation