本文へ移動
cccskills
無料GitHub で公開

document-memory-summarize

Summarize extracted paper text into concise Markdown memory for later Stage 2 and Stage 4 background use, with fixed section headings and no fabricated claims.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md1.1 KB
  • scripts/extract_pdf_text.py1.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Document Memory Summarize

Goal

Convert extracted document text into a concise Markdown memory file that is easy to edit and reuse in later rebuttal stages.

Input

  • document_text: plain text extracted from .txt, .md, .tex, or .pdf.

Output

Return strict JSON:

{ "markdown": "..." }

Required Markdown headings

  • ## Contribution Overview
  • ## Introduction
  • ## Methods
  • ## Logic Chain
  • ## Experiments
  • ## Key Conclusions

Rules

  1. Keep the Markdown concise and scannable.
  2. Preserve the main theoretical ideas, methods, experiments, and conclusions.
  3. Ignore references, appendices, and supplementary material unless the main text clearly depends on them.
  4. Do not invent numbers, citations, experiments, or claims.
  5. If a section is weak or missing in the source, keep the heading and add a short grounded note instead of guessing.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Verification guide for citations added during rebuttal writing. Use when Stage 2 responses introduce new references, when the Area Chair asks about a cited paper, or when any citation in the rebuttal might have been AI-generated. Prevents the serious credibility damage of fabricated references in reviewer-facing documents.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

Systematic quality check for a completed rebuttal document before submission. Use after Stage 3 document compilation or after Stage 5 final remarks writing to verify completeness, tone, factual accuracy, and structural integrity. Catches common rebuttal errors before they reach reviewers.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

Systematic review response strategy guide for RebuttalStudio. Use when developing response strategies for reviewer comments, deciding how to classify concerns, or choosing between Accept/Defend/Clarify/Experiment approaches at any stage of the rebuttal pipeline.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

skills

無料

Multi-stage rebuttal analysis skill for RebuttalStudio. Use when organizing reviewer comments into stage-specific conference workflows, including stage1 breakdown, stage2 refinement, stage4 multi-round follow-up, and stage5 final remarks generation.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted issues.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

Base template for Stage1 reviewer-comment breakdown. Use when creating or extending conference-specific Stage1 skills (for example ICLR, ICML, NeurIPS, ACL) so shared extraction, splitting, and response-mapping logic stays consistent.

日本語の概要は準備中です。原文の説明を表示しています。

runtsang/RebuttalStudio2182026年4月4日 更新

runtsang のスキルをすべて見る

このスキルの問題を報告する