Self Review

by lingzhi2279e6c085d65e3No license386 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 7 months ago

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.

AI-generated overview

Reviews an academic paper with three reviewer personas, ensemble scoring and reflection, producing structured feedback.

What it does
Extracts text from a PDF or reads a LaTeX source, then runs three independent reviewer personas against the NeurIPS review form. Each review is refined through up to three reflection rounds, and the results are aggregated with weighted scores into a meta-review. It outputs a report with scores, decision, consensus strengths and weaknesses, questions for authors, and section-specific suggestions.
When to use it
Use when an author wants feedback on a paper draft before submission. It suits checking a manuscript against a structured conference review form and identifying missing sections or weak points.
Requirements
Python with pymupdf4llm, pymupdf and pypdf for PDF extraction; the skill ships two executable scripts. It reads a local PDF or .tex file and a bundled review-form reference.

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.txtpython ~/.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):

  1. Harsh but fair reviewer: Expects good experiments that lead to insights
  2. Harsh and critical reviewer: Looking for impactful ideas in the field
  3. 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 Authors1. ...
## Specific Suggestions for Improvement1. [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

Source and attribution

Source:lingzhi227/agent-research-skillsinskills/self-reviewat commit9e6c085

License: No license

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