deep-read-template

Literature Deep-Read Report

Overview

This skill converts a research paper into a reusable, seven-part deep-read report that a graduate student, reviewer, or researcher can file and cite. It enforces a consistent scaffold — background, variables, paradigm/method, task flow, design details, statistical analysis, main results — so nothing critical is missed and papers become comparable across a reading list. The skill reads the actual paper text; it does not invent findings.

When to Use

Activate this skill when the user:

• Attaches or links a paper (PDF, DOI, arXiv, PubMed, or journal URL) and asks for a "精读", "文献报告", "深度总结", or "deep-read".
• Asks to extract a study's method, variables, design, or results for a literature review.
• Says "分析这篇论文的设计/统计/范式" or "帮我整理这篇文献".
• Wants a comparable one-page-per-paper note for a reading list.

Do not activate for: pure citation formatting, plagiarism checks, or non-academic documents. If the user only wants a one-line summary, still deliver the full scaffold but keep each section brief.

Execution Logic

Step 1 — Acquire the paper text

Resolve the input in this priority order:

1. Local PDF path → extract text (try a PDF text tool; if unavailable, ask the user to paste the text).
2. DOI or arXiv/PubMed link → fetch the abstract and, if open access, the full text.
3. Title only → search the web for the PDF/abstract and confirm with the user before proceeding.

If the full text is not obtainable, build the report from the abstract + available sections and explicitly mark missing parts as "全文未获取，以下基于摘要与公开信息".

Step 2 — Parse the structure

Map the paper's real sections (Introduction, Methods, Results, Discussion) onto the seven required components. Do not force a mismatch — if a component is genuinely absent, say so.

Step 3 — Extract each component

Use the scaffold in references/deep-read-template.md and fill:

• 背景 (Background): the gap, research question, and why it matters.
• 变量 (Variables): independent, dependent, moderators/mediators, control variables, and how each was measured.
• 范式方法 (Paradigm / Method): the overarching paradigm (e.g., RL + DDM computational modeling, fMRI, survey, experiment) and the modeling or analytical approach.
• 任务流程 (Task Flow): the participant's step-by-step experience in the experiment or the pipeline of the analysis.
• 设计细节 (Design Details): design type (between/within), sample size, stimuli, trials, counterbalancing, software, parameters.
• 统计分析 (Statistical Analysis): tests used, models, corrections, power, and what each test answered.
• 主要结果 (Main Results): the key findings with effect sizes / estimates and their interpretation.

Step 4 — Render and verify

Output the seven-section report in markdown. Quote exact numbers from the paper (with section/page if possible). Add a one-line "一句话贡献" at the top and a "局限与可复现性" note at the end. If any extracted claim is uncertain, flag it.

Output Structure

The report must contain these seven headings (in this order), prefixed by a one-line contribution:

1. 一句话贡献 (One-line Contribution)
2. 背景 (Background)
3. 变量 (Variables)
4. 范式方法 (Paradigm / Method)
5. 任务流程 (Task Flow)
6. 设计细节 (Design Details)
7. 统计分析 (Statistical Analysis)
8. 主要结果 (Main Results)
9. 局限与可复现性 (Limitations & Reproducibility)

A filled template and a worked computational-modeling example are in references/deep-read-template.md.

Reliability Rules

• Quote the paper; never hallucinate statistics, p-values, or sample sizes.
• Mark every inferred (non-explicit) claim as "推断".
• If the text is unavailable, state it and degrade gracefully to an abstract-based report.
• Preserve the authors' original terminology for variables and methods; add Chinese glosses in parentheses.

Resources

• references/deep-read-template.md — the seven-section scaffold with per-section extraction prompts and a worked example.