Token导航 LogoToken导航

Agent Skill · 研究检索

paper-highlight(论文亮点)

paper-highlight 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

paper-highlight 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 22,142。

总安装 4,647周安装 188GitHub Stars 公开资料未说明下载 1,459核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paper-highlight(论文亮点)
来源仓库:https://github.com/yorch233/paper-highlight
安装命令:
openclaw skills install paper-highlight
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install paper-highlight

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

按语义类别自动突出显示论文重点内容。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。将论文划分为目标、方法、结果等五大模块。帮助用户快速浏览与定位关键信息段落。需确认是否依赖特定解析引擎及其资源消耗情况。paper-highlight 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install paper-highlight;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

paper-highlight 怎么安装?

在终端执行 openclaw skills install paper-highlight。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

paper-highlight 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

paper-highlight 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

paper-highlight 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

paper-highlight 用的人多吗?

公开数据是累计安装 4,647 次,近一周安装 188 次,下载 1,459 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

paper-highlight 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
paper-highlight

论文亮点 · 本页

4,647—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
paper-highlight
description
Automatically highlight academic papers by 5 semantic categories — goal, motivation, method, results, contributions — to help you quickly skim a paper. Configurable density levels, opacity, and optional note layers.
metadata
{"openclaw":{"emoji":"🖍️","requires":{"bins":["python3","uv"]},"install":[{"id":"brew","kind":"brew","formula":"uv","bins":["uv"],"label":"Install uv (brew)"}]}}

Paper Highlight

Use this skill as a paper-reading workflow: understand first, then mark reusable information with stable semantics. Tune density with the highlight level, but keep each color meaning consistent.

Setup

This skill needs a one-time setup after installation. Prefer uv so the dependency stays isolated and reproducible:

cd /Users/yo/.codex/skills/paper-highlight
uv venv .venv
source .venv/bin/activate
uv pip install pymupdf

If the environment already exists, refresh it with:

cd /Users/yo/.codex/skills/paper-highlight
source .venv/bin/activate
uv pip install --upgrade pymupdf

Workflow

  1. Read references/highlight-rules.md before annotating.
  2. Do a preflight inspection before any model-heavy reading. Surface the PDF page count, extracted text volume, rough word count, and a rough token estimate to the user.
  3. Explicitly warn that full-paper pre-reading can consume a large number of tokens, especially on long PDFs or dense two-column papers. Do not continue until the user clearly agrees.
  4. Inspect the PDF structure once the user agrees. Prefer abstract, introduction, method overview, main results, and conclusion before touching fine details.
  5. Build a mental highlight plan across the core reading chain: goal -> motivation -> method -> results. Add contribution, setup, or limitation colors only when they are independently reusable.
  6. Run the bundled PyMuPDF script to annotate the PDF with semantic colors. Use light opacity by default; it now uses soft pastel colors at 0.5 opacity so the marks stay visible without fully covering the text. Switch to dark only when the source PDF is faint or image-heavy.
  7. Add the optional explanation layer when needed: a paper-level TLDR paragraph and concise section flow summaries. Do not quote the original paper text inside notes; rewrite it as clear, compact summaries.
  8. Review the output and remove noisy marks if the page looks busy. Prefer under-highlighting over over-highlighting.

Preflight Gate

Before executing the full highlighting workflow, report at least:

  • page count
  • extracted character count
  • extracted word count
  • rough token estimate

Use those numbers to tell the user that the paper may be expensive to process. If the user does not explicitly confirm, stop after the inspection step.

Selection Rules

  • Highlight only sentences that remain useful when revisiting the paper later for writing, reviewing, rebuttal, or method comparison.
  • Favor self-contained sentences that summarize a paragraph, state a claim, define a setup, or express a takeaway.
  • Skip transitions, generic field background, implementation trivia, decorative prose, and sentences that only feel important in the moment.
  • Keep one semantic meaning per color. Do not reuse a color for a neighboring but different category.
  • Stay useful even at higher density. Use the level presets to control how aggressive the pass should be, then trim obvious noise if a page becomes crowded.

Highlight Levels

Use --highlight-level to control annotation density:

  • low: fixed baseline
  • medium: target about 1.25x the low count
  • high: target about 1.5x the low count
  • extreme: target up to 2x the low count

Manual flags such as --max-per-page, --max-total, and --min-score still override the level preset when needed.

Note Modes

Use --note-mode to control the explanation layer:

  • default: add a title TLDR paragraph and concise section flow summaries
  • none: disable all note annotations

Color Policy

Use the required 5-color core by default:

  • Yellow: research goal or problem definition
  • Orange: motivation, gap, challenge, or prior limitation
  • Blue: main method or core mechanism
  • Pink: contributions or novelty claims
  • Green: main results or conclusions

Enable optional colors only when they clearly add value:

  • Purple: definitions, assumptions, notation, or evaluation setup
  • Gray: limitations, caveats, failure cases, or future work

Commands

Set up the local environment:

cd /Users/yo/.codex/skills/paper-highlight
uv venv .venv
source .venv/bin/activate
uv pip install pymupdf

Run the highlighter:

cd /Users/yo/.codex/skills/paper-highlight
source .venv/bin/activate
python3 scripts/highlight_paper.py input.pdf
python3 scripts/highlight_paper.py input.pdf --highlight-level medium
python3 scripts/highlight_paper.py input.pdf --highlight-level high --include-optional
python3 scripts/highlight_paper.py input.pdf --note-mode flow
python3 scripts/highlight_paper.py input.pdf --note-mode hightlight
python3 scripts/highlight_paper.py input.pdf --note-mode full
python3 scripts/highlight_paper.py input.pdf --highlight-level exteme --opacity dark
python3 scripts/highlight_paper.py input.pdf --disable-optional definitions

Ask the script for its exact CLI when needed:

cd /Users/yo/.codex/skills/paper-highlight
source .venv/bin/activate
python3 scripts/highlight_paper.py --help

Resources

  • references/highlight-rules.md: color legend, density targets, and sentence-level filters
  • scripts/highlight_paper.py: bundled PyMuPDF annotator for the final PDF pass

平台分布

OpenClaw

76.5%
按下载量换算1,116

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills