About this skill

Problem

Algorithm papers often compress model architecture, training objectives, and experimental conclusions into a dense page count. Readers who only scan abstracts can miss important design decisions, while close reading is interrupted by equations, notation, and module names. This skill targets algorithm and computer science papers on Arxiv, aiming to turn a paper into a deliverable long-form technical interpretation rather than a short summary. It fits cases where the user provides a paper title or ID and needs a professional, detailed, paragraph-level breakdown; it is not intended for quick three-sentence summaries or non-algorithm papers.

How It Works

Atomized reading: It first attempts to fetch the PDF from Arxiv, with retries or a human-provided file when retrieval fails; then it indexes paragraphs across Abstract, Intro, Methodology, and Experiments to avoid skipping content.
Template-driven generation: The output includes a title, TLDR, problems, proposed solution, benefits, model architecture, and experiments. For architecture, each sub-module starts with a one-sentence role summary, followed by input processing, data flow, core logic, and output prediction.
Forced reflection: Before final output, it checks template coverage, concision, architecture depth, formula removal, paragraph coverage, and logical closure, then fills gaps.
Boundary control: It may use minimal symbols such as S for sequences, prohibits mathematical formulas, and marks uncertain parts as [needs verification] instead of inventing conclusions.

Screenshots

Use Cases

When reading an Arxiv recommender paper, turn model modules and data flow into a formula-free long-form note.
Before a group meeting, generate an architecture breakdown and experiment summary with section references from a paper ID.
When assessing algorithmic improvements, extract pain points, methods, benefits, and ablation results.
Convert complex PDF architecture into reusable, equation-free technical blog material.

Best For

ML grad students preparing group meetings who need a formula-free architecture walkthrough.
Engineers writing technical explainers who need Arxiv papers turned into publishable long-form notes.
AI product managers evaluating competitor algorithms who need methods, benefits, and experiment results.
Algorithm engineers doing deployment research who need to verify data flow and ablation experiments.

Related Skills

Scholar Reasoning Distiller

@user_231765ee/django-plus

Extracts an eight-layer reasoning fingerprint from scholar texts and applies a nine-module constraint to run peer review, supervision, lecturing, or panel discussion with quantitative scores and qualitative comments.

Knowledge Management 2026.08.30 ↓ 608