paper-reader

To convert academic papers into structured Markdown, this skill ingests the document and outputs a reader-friendly overview alongside a mathematically rigorous deep read. It processes the paper end to end, ensuring fidelity through per-chunk self-checks and knowledge mapping.

Yes, you can generate citation-ready deep reads from academic papers. The skill performs equation-aligned, image-grounded verification and runs a recursive self-check loop to ensure formal traceability and mathematical rigor for publication or review.

Extracting contributions and methodologies involves orchestrated sub-skills that probe prerequisites, walk pipelines, and explain math. This approach transforms the paper into structured outputs, mapping knowledge to ensure explanations are tailored and verified against the source.

Knowledge mapping improves paper reading fidelity by aligning explanations to the document's specific equation numbers and images. It enforces a strict protocol with a recursive self-check loop, verifying each chunk to maintain traceability and prevent misinterpretation.

When processing complex mathematical papers, the structured output relies on explicit triggers and eight sub-skills. While it enforces per-chunk self-checks and image-grounded verification, complex papers require strict alignment to equation numbers to maintain fidelity and avoid traceability issues.