paper-reading

Produces structured critical reading notes from research paper PDFs with cropped figures and citations.

52|Updated Apr 24, 2026
One-click install
npx skills add https://github.com/Immortalqx/my_codex_skills --skill paper-reading-immortalqx
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: paper-reading
Source: https://github.com/Immortalqx/my_codex_skills/tree/main/paper-reading
Command: npx skills add https://github.com/Immortalqx/my_codex_skills --skill paper-reading-immortalqx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymupdf, pillow, and includes scripts (resource) and references (resource) components.

What problem does it solve? Reading a research paper deeply takes hours, and quick summaries miss benchmark misuse, overclaims, and weak evidence. This Skill turns a single paper PDF into a rigorous, citation-backed reading note you can trust months later. ## Core Features & Use Cases - Five-phase deep reading workflow: skim and frame, benchmark audit, prior-art matrix, deep re-read with figure capture, and final note synthesis. - Benchmark audit and critical checks: verifies each benchmark's original task against the paper's usage, flags overclaims, unevaluated scenarios, and reproducibility gaps. - PDF tooling scripts: extract text with page markers, split sections heuristically, render pages to PNG, and auto-crop figures with manual bbox fallback. - Use Case: Hand the Skill an arXiv PDF of a new segmentation paper and receive a 9-section note with cropped figures, a related-work matrix of up to 15 papers, and benchmark-mismatch warnings. ## Quick Start Use the paper-reading skill to deeply read this paper PDF and produce a structured critical reading note with cropped figures and numbered citations.

Frequently Asked Questions about paper-reading

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a structured reading note from a research paper PDF?

Provide the PDF, arXiv ID, or URL and ask for a deep reading. The skill runs five phases: framing, benchmark audit, prior-art matrix, deep re-read with figure cropping, and synthesis into a 9-section note saved under readings/<slug>/.

What tools extract text and figures from PDF papers?

The bundled scripts use PyMuPDF as the primary backend with Poppler's pdftotext and pdftoppm as fallbacks. Pillow handles figure cropping, and locate_figures.py finds captions automatically with a manual --bbox override for bad crops.

Does this skill work for literature surveys or quick paper overviews?

No. It is scoped to deep reading of a single paper. Quick one-paragraph overviews belong to a lookup skill, broad literature surveys to a survey skill, and simulated peer review to a review skill.

Why does figure cropping produce wrong or clipped images?

Auto-crop locates the caption and crops above it, which fails with multi-column layouts or unusual caption placement. Re-run locate_figures.py with --bbox "x,y,w,h" in PNG pixels to specify the crop region manually.

Where are downloaded papers and the final note stored?

PDFs go into the project's paper directory (papers/ by default), the final note goes to readings/<slug>/ with its figures, and intermediate artifacts stay in x_temp/paper-reading/<slug>/. Nothing is written into the skill folder.