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Agent skill

Summary

by alaliqing in alaliqing/claude-paper

Use this for a quick summary of a research paper's core ideas and key points.

MITAuto-check: notesResearch & Science

Install Summary

skills CLI
$ npx skills add alaliqing/claude-paper --skill summary -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alaliqing/claude-paper summary --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alaliqing/claude-paper.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/summary .claude/skills/summary && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
summary
GitHub stars
344
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
486 words
Files
1
Skills in repo
3
Repo updated
First seen
Licence
MIT

At a glance

Use this for a quick summary of a research paper's core ideas and key points.

  • Works in 6 steps: Check Dependencies (First Run Only) → Download and Parse PDF → Generate Quick Summary → …
  • You want to quickly understand a paper without deep study materials
  • SKILL.md covers Step 1a: Check input type and… and Step 1b: Parse PDF
  • Calls node, npm and python3; reaches arxiv.org and github.com

What it does

Summary is an agent skill from alaliqing/claude-paper. Use this for a quick summary of a research paper's core ideas and key points. Use when you want to quickly understand a paper without deep study materials. Triggers on PDF paths, arXiv URLs, or paper URLs.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Academic paper search and PDF. It works with arXiv, DeepSeek, npm and Vue.js. The repository describes itself as: 📖 Cross-agent research paper toolkit for Claude Code, Codex, OpenCode, and DeepSeek Harness—quick summaries, deep study materials, code demos, and a local web viewer. The licence is MIT.

When your agent uses it

  • You want to quickly understand a paper without deep study materials
  • Tasks that involve Academic paper search
  • Tasks that involve PDF

Example prompts

  • “/summary”

Requirements

  • Python 3
  • Node.js
  • Pre-approved tools (allowed-tools): Bash, Write, Read

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Check Dependencies (First Run Only)
  2. Download and Parse PDF
  3. Generate Quick Summary
  4. Update Index
  5. Relaunch Web UI
  6. Present Summary to User

What it can do on your machine

Read from SKILL.md and the folder at commit 0af55d0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Write
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • node
    • npm
    • python3
    • pip3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • arxiv.org
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Summary loads about 2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 486 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Write, Read

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alaliqing/claude-paper at commit 0af55d0, republished under its MIT licence (© alaliqing). 486 words, ~2,034 tokens.

Download SKILL.mdSave it as .claude/skills/summary/SKILL.md (or your agent's skills folder).
name
summary
description
Use this for a quick summary of a research paper's core ideas and key points. Use when you want to quickly understand a paper without deep study materials. Triggers on PDF paths, arXiv URLs, or paper URLs.
allowed-tools
Bash, Write, Read
disable-model-invocation
false

Quick Paper Summary Workflow

This skill generates a concise summary of a research paper's core ideas and key points.

When to use:

  • You want to quickly understand what a paper is about
  • You need the main contributions without deep technical details
  • You're screening papers to decide which to study in depth

When NOT to use:

  • You want comprehensive study materials (use /claude-paper:study instead)
  • You need code demonstrations
  • You want interactive visualizations

Language Detection: Detect the user's language from their input and generate ALL materials in that language.

  • Example: User says "我们学习一下这篇论文" → Generate materials in Chinese
  • Example: User says "Let's study this paper" → Generate materials in English

Step 0: Check Dependencies (First Run Only)

bash
if [ ! -f "${CLAUDE_PLUGIN_ROOT}/.installed" ]; then
  echo "First run - installing dependencies..."
  cd "${CLAUDE_PLUGIN_ROOT}"
  npm install || exit 1

  # Install Python dependencies for image extraction
  python3 -m pip install pymupdf --user 2>/dev/null || pip3 install pymupdf --user 2>/dev/null || echo "Warning: Failed to install pymupdf"

  touch "${CLAUDE_PLUGIN_ROOT}/.installed"
  echo "Dependencies installed!"
fi

Step 1: Download and Parse PDF

Supports multiple input formats:

  • Local path: ~/Downloads/paper.pdf
  • Direct PDF URL: https://arxiv.org/pdf/1706.03762.pdf
  • arXiv URL: https://arxiv.org/abs/1706.03762

Step 1a: Check input type and download if URL

bash
USER_INPUT="<user-input>"

# Check if input is a URL (starts with http:// or https://)
if [[ "$USER_INPUT" =~ ^https?:// ]]; then
  # Download PDF from URL
  INPUT_PATH=$(node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/download-pdf.cjs "$USER_INPUT")
else
  # Use local path directly
  INPUT_PATH="$USER_INPUT"
fi

For URLs, the download script will:

  • Download PDFs to /tmp/claude-paper-downloads/
  • Convert arXiv /abs/ URLs to PDF URLs automatically
  • Validate that URLs point to PDF files
  • Return the local file path for processing

Step 1b: Parse PDF

Extract structured information:

bash
PARSE_OUTPUT_DIR=$(mktemp -d)
node ${CLAUDE_PLUGIN_ROOT}/skills/study/scripts/parse-pdf.js \
  "$INPUT_PATH" \
  --output-dir "$PARSE_OUTPUT_DIR"

The command prints a small, strict JSON summary to stdout and writes:

  • meta.json — title, authors, abstract, links, page count, and a context-safe content preview
  • paper.txt — complete extracted text without the 50k preview limit

Use paper.txt as the source for the quick summary. Do not treat meta.json.content as the complete paper when contentTruncated is true.

Step 2: Generate Quick Summary

Create the paper folder:

bash
mkdir -p ~/claude-papers/papers/{paper-slug}
cp "<metaPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/meta.json
cp "<fullTextPath-from-parser-output>" ~/claude-papers/papers/{paper-slug}/paper.txt
cp "$INPUT_PATH" ~/claude-papers/papers/{paper-slug}/paper.pdf

Generate quick-summary.md with the following structure:

markdown
# Quick Summary: [Paper Title]

## One Sentence
[One sentence that captures what the paper is about]

## Problem
[What problem does this paper solve? Why is it important?]

## Core Idea
[The key innovation explained in 2-3 sentences. What makes this paper novel?]

## Key Contributions
- [Contribution 1]
- [Contribution 2]
- [Contribution 3]
- [Contribution 4 if applicable]

## Main Results
| Metric | Value | Dataset/Benchmark |
|--------|-------|-------------------|
| [metric1] | [value] | [dataset] |
| [metric2] | [value] | [dataset] |

## Why It Matters
[Practical implications. How does this advance the field? What can we now do that we couldn't before?]

## Limitations
- [Limitation 1]
- [Limitation 2]

Guidelines for each section:

SectionLengthFocus
One Sentence1 sentenceHigh-level summary
Problem2-3 sentencesContext and motivation
Core Idea2-3 sentencesThe main innovation
Key Contributions3-5 bulletsWhat's new/novel
Main Results1 tableQuantitative metrics from the paper
Why It Matters2-3 sentencesPractical value
Limitations2-3 bulletsWhat the paper doesn't solve

Total length: ~300-500 words (excluding results table)

Show full SKILL.md (173 more words)Show less

Step 3: Update Index

CRITICAL: Read existing index.json first, then append the new paper. Never overwrite the entire file.

If index.json does not exist, create:

json
{"papers": []}

Append new entry to the papers array:

json
{
  "id": "paper-slug",
  "title": "Paper Title",
  "slug": "paper-slug",
  "authors": ["Author 1", "Author 2"],
  "abstract": "Paper abstract...",
  "year": 2024,
  "date": "2024-01-01",
  "tags": ["quick-summary"],
  "githubLinks": ["https://github.com/..."],
  "codeLinks": ["https://..."]
}

IMPORTANT: The index.json file must be located at:

~/claude-papers/index.json

Step 4: Relaunch Web UI

Invoke:

/claude-paper:webui

Step 5: Present Summary to User

After generating the summary:

  1. Show the user the quick-summary.md content - Display the full summary

  2. Offer next steps:

    • "Would you like to study this paper in more depth? Use /claude-paper:study for comprehensive materials."
    • "Do you have questions about specific parts of the paper?"
    • "Would you like me to explain any section in more detail?"

  3. File location reminder:

    • Summary saved to: ~/claude-papers/papers/{paper-slug}/quick-summary.md
    • Web UI available at: http://localhost:5815

Example Output

markdown
# Quick Summary: Attention Is All You Need

## One Sentence
This paper introduces the Transformer, a neural network architecture based entirely on attention mechanisms, achieving state-of-the-art results in machine translation.

## Problem
Sequence transduction models at the time (RNNs, LSTMs, GRUs) process data sequentially, limiting parallelization and struggling with long-range dependencies.

## Core Idea
Replace recurrent layers with self-attention mechanisms, enabling full parallelization during training and direct modeling of dependencies regardless of distance. The Transformer uses multi-head attention to jointly attend to information from different representation subspaces.

## Key Contributions
- First transduction model relying entirely on self-attention, no recurrence
- Multi-head attention mechanism for joint attention across subspaces
- Positional encodings to inject sequence order information
- Achieved 28.4 BLEU on WMT 2014 English-to-German (2+ BLEU improvement)
- Training was significantly faster than previous state-of-the-art

## Main Results
| Metric | Value | Dataset/Benchmark |
|--------|-------|-------------------|
| BLEU (EN-DE) | 28.4 | WMT 2014 |
| BLEU (EN-FR) | 41.8 | WMT 2014 |
| Training cost | 3.3 × 10^18 FLOPs | WMT 2014 EN-DE |
| Training time | 12 hours on 8 P100 | WMT 2014 EN-DE |

## Why It Matters
The Transformer eliminated recurrence, enabling massive parallelization and scaling. This architecture became the foundation for BERT, GPT, and virtually all modern large language models, fundamentally changing NLP and beyond.

## Limitations
- Self-attention has O(n²) complexity, limiting sequence length
- No explicit modeling of position beyond learned encodings
- Requires large amounts of training data

Notes

  • This skill is intentionally minimal - it generates only the summary, no code demos, no interactive HTML, no deep-dive materials
  • For users who want more, they can use /claude-paper:study to generate comprehensive materials
  • The summary should be self-contained and readable in under 5 minutes
  • Focus on conceptual clarity over technical details

© alaliqing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/summary of alaliqing/claude-paper.

Open the folder on GitHubat commit 0af55d0

Used in 1 other repository

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alaliqing/claude-paper, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Summary

What does Summary do?

Use this for a quick summary of a research paper's core ideas and key points. Summary is an agent skill from alaliqing/claude-paper. Use this for a quick summary of a research paper's core ideas and key points.

When should I use Summary?

Summary fits situations like: you want to quickly understand a paper without deep study materials; tasks that involve Academic paper search; tasks that involve PDF.

How do I install Summary in Claude Code?

Run `npx skills add alaliqing/claude-paper --skill summary -a claude-code`. Or copy the skill folder (plugin/skills/summary in alaliqing/claude-paper) into .claude/skills/summary in your project. Claude Code loads it when a task matches its description.

How do I install Summary in Codex?

Run `npx skills add alaliqing/claude-paper --skill summary -a codex`. Or copy the skill folder (plugin/skills/summary in alaliqing/claude-paper) into .agents/skills/summary in your project. Codex loads it when a task matches its description.

Can I use Summary in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alaliqing/claude-paper --skill summary -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/summary, .gemini/skills/summary, .github/skills/summary and .opencode/skills/summary in your project.

What does Summary need to run?

Going by SKILL.md and its folder, Summary needs the command-line tools its instructions call (node, npm, python3 and pip3). Our summary lists: Python 3; Node.js. Its frontmatter pre-approves these tools: Bash, Write, Read.

Does Summary access the network?

SKILL.md names 2 domains. In commands or code: arxiv.org and github.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Summary safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Summary use?

Summary is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Summary use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Summary?

Skills that share tags, products or a category with Summary: Paper Reading (Edwardxlai/easyread, 848 stars), Ref Downloader (ltczding-gif/ref-downloader, 139 stars), Paper Reading (sodalone/paper-reading-skill, 142 stars) and Paper Explainer (ZJU-REAL/Easel, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Summary?

alaliqing (a GitHub user) maintains it in alaliqing/claude-paper, which has 344 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on August 14, 2026.

Source: alaliqing/claude-paper on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.