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Agent Skill · 研究检索

paper-view-skill(论文查看技巧)

paper-view-skill 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-view-skill 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 19,807。

总安装 5,457周安装 232GitHub Stars 公开资料未说明下载 1,912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
1.0.2

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-view-skill(论文查看技巧)
来源仓库:https://github.com/yyccr/paper-view-skill
安装命令:
openclaw skills install paper-view-skill
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-view-skill

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。

从论文数据生成 ECharts 可视化和文字云图表。支持文本、数据和 PDF 内容的多种输入方式。提供 AI 科学图表和可视化内容创建功能。需确认图表生成 API 和数据处理权限。建议检查输出格式和图表样式定制选项。paper-view-skill 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-view-skill;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

paper-view-skill 怎么安装?

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

paper-view-skill 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-view-skill 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

paper-view-skill 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-view-skill 用的人多吗?

公开数据是累计安装 5,457 次,近一周安装 232 次,下载 1,912 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-view-skill 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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-view-skill

论文查看技巧 · 本页

5,457—接触敏感数据MIT-0有提醒

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
paperview
description
PaperView API — generate ECharts visualizations, AI scientific diagrams, and word clouds from data, text, or PDF papers. Use when the user wants to create charts, scientific figures, flowcharts, architecture diagrams, or extract keywords from documents.
author
yyccR
homepage
https://www.ipaperview.com
repository
https://github.com/yyccR/paper-view-skill
license
MIT
env
PAPERVIEW_API_TOKEN
description
API token obtained from www.ipaperview.com (Profile → API Token). Format: pv_live_<hex_string>
required
true

PaperView API

PaperView provides three AI-powered visualization APIs:

  1. ECharts Visualization — generate interactive charts from CSV/JSON/text data
  2. AI Scientific Diagram — generate publication-quality figures from text or arxiv PDF
  3. Word Cloud — extract keyword frequencies from PDF documents (supports CJK)

Authentication

All requests require an API Token:

Authorization: Bearer pv_live_<your_token>

Obtain your token from the www.ipaperview.com website (Profile → API Token). Each account can create one token. Set via environment variable PAPERVIEW_API_TOKEN or ask the user for their token.

Base URL

https://api.ipaperview.com

API Quota

Daily API call limits by plan:

PlanDaily Limit
Free3 calls/day
Monthly ($4.99/mo)30 calls/day
Yearly ($29.99/yr)100 calls/day

Each API call consumes 1 quota.


1. ECharts Visualization

POST /api/v1/viz/generate/

AI analyzes your data sample, selects the best chart type, and returns a ready-to-render ECharts option. The full data processing happens server-side — AI only sees a sample to generate a transform script, then the backend executes it with Node.js on the full dataset.

FieldTypeRequiredDescription
datastringYesRaw data in CSV, JSON, or plain text
toolsetstringNoChart category: bar, line, scatter, pie, heatmap, violin, manhattan, volcano, forest, survival, roc, venn, upset, etc. If omitted, AI auto-selects
templatestringNoSpecific template within the toolset. If omitted, AI auto-selects
contextstringNoNatural language instructions, e.g. "show GDP trend by country", "use blue color scheme"

Available chart types:

  • Basic: line, bar, scatter, pie, radar, funnel, boxplot
  • Advanced: heatmap, treemap, sankey, tree, sunburst, map, structure
  • 3D: gl3d (bar3D, scatter3D)
  • Scientific: violin, manhattan, volcano, forest, survival, roc, venn, upset, enrichment, circos, waterfall
curl -X POST \
  -H "Authorization: Bearer $PAPERVIEW_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "data": "Year,China,USA,Japan\
2018,13608,20544,4971\
2019,14280,21373,5082\
2020,14723,20894,5040\
2021,17734,23315,4941\
2022,17963,25463,4231",
    "context": "Show GDP trends as a line chart"
  }' \
  https://api.ipaperview.com/api/v1/viz/generate/

Response:

{
  "success": true,
  "toolset": "line",
  "template": "confidence_band",
  "echarts_option": {
    "title": { "text": "GDP by Country (2018-2022)" },
    "xAxis": { "type": "category", "data": ["2018", "2019", "2020", "2021", "2022"] },
    "yAxis": { "type": "value" },
    "series": [
      { "name": "China", "type": "line", "data": [13608, 14280, 14723, 17734, 17963] },
      { "name": "USA", "type": "line", "data": [20544, 21373, 20894, 23315, 25463] },
      { "name": "Japan", "type": "line", "data": [4971, 5082, 5040, 4941, 4231] }
    ],
    "legend": { "data": ["China", "USA", "Japan"] }
  },
  "reason": "Time series data with multiple countries — line chart shows trends clearly"
}

The echarts_option can be rendered directly with echarts.setOption(echarts_option) in any ECharts-compatible environment.


2. AI Scientific Diagram

POST /api/diagram/ai-generate/

Generate publication-quality scientific diagrams, flowcharts, and research illustrations. Supports arxiv paper URLs directly. Returns a Server-Sent Events stream — use curl -N to receive events.

FieldTypeRequiredDescription
template_typestringYescustom_text_only (from text) or custom (with reference image)
custom_promptstringYesDescription of what to draw. Can include style instructions (colors, themes, specific elements)
pdf_urlstringNoURL to a PDF paper (supports arxiv abs/pdf URLs like http://arxiv.org/abs/2510.13809v1)
selected_textstringNoText excerpt to visualize
reference_image_urlstringNoReference image URL to guide the style
languagestringNoauto, en, zh (default: auto)
aspect_ratiostringNoe.g. 16:9, 1:1, 4:3

Example — from text description:

curl -N -X POST \
  -H "Authorization: Bearer $PAPERVIEW_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "template_type": "custom_text_only",
    "custom_prompt": "A flowchart: Data Collection -> Preprocessing -> Training -> Evaluation"
  }' \
  https://api.ipaperview.com/api/diagram/ai-generate/

Example — from arxiv paper with custom style:

curl -N -X POST \
  -H "Authorization: Bearer $PAPERVIEW_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "template_type": "custom_text_only",
    "pdf_url": "http://arxiv.org/abs/2510.13809v1",
    "custom_prompt": "Generate an architecture diagram with pink color scheme, include Doraemon as a mascot element"
  }' \
  https://api.ipaperview.com/api/diagram/ai-generate/

SSE Events (in order):

data: {"type": "step", "step": "extracting", "message": "Extracting document content..."}
data: {"type": "step", "step": "generating_prompt", "message": "Generating image prompt..."}
data: {"type": "prompt_generated", "prompt": "...", "enhanced_prompt": "..."}
data: {"type": "step", "step": "generating_image", "message": "Generating image..."}
data: {"type": "complete", "success": true, "image_url": "https://...", "model": "gemini-2.0-flash-preview-image-generation"}

The final complete event contains image_url — a CDN URL to the generated image.


3. Word Cloud

POST /api/wordcloud/extract/

Extract keyword frequencies from PDF documents with automatic CJK (Chinese/Japanese/Korean) segmentation and semantic clustering.

FieldTypeRequiredDescription
pdf_urlstringYesURL to a PDF document (supports arxiv URLs)
max_wordsintegerNoMax keywords to return (default: 100)
curl -X POST \
  -H "Authorization: Bearer $PAPERVIEW_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "pdf_url": "https://arxiv.org/pdf/2301.00001.pdf",
    "max_words": 50
  }' \
  https://api.ipaperview.com/api/wordcloud/extract/

Response:

{
  "success": true,
  "data": [
    { "word": "neural", "frequency": 45, "cluster": 0 },
    { "word": "network", "frequency": 38, "cluster": 0 },
    { "word": "attention", "frequency": 32, "cluster": 1 }
  ]
}

The cluster field (0-4) groups words by frequency — useful for color coding in word cloud rendering. CJK documents are automatically segmented using jieba.


Error Responses

{ "success": false, "error": "Error description" }
HTTP CodeMeaning
400Bad request (missing or invalid parameters)
401Invalid or missing API token
402Quota exceeded (daily API call limit reached)
500Server error

Rendering Results

After calling the API, you MUST render the results visually. No extra dependencies needed — use CDN-loaded HTML files.

Render ECharts Visualization

When you receive echarts_option from /api/v1/viz/generate/, create a self-contained HTML file and open it in the browser:

<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>PaperView Chart</title>
  <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script>
  <style>body{margin:0;background:#fff}#chart{width:100vw;height:100vh}</style>
</head>
<body>
  <div id="chart"></div>
  <script>
    var chart = echarts.init(document.getElementById('chart'));
    var option = {{ECHARTS_OPTION_JSON}};
    chart.setOption(option);
    window.addEventListener('resize', function() { chart.resize(); });
  </script>
</body>
</html>

Replace {{ECHARTS_OPTION_JSON}} with the echarts_option value from the API response (the JSON object, not stringified). Save as a .html file and open:

# macOS
open /tmp/paperview_chart.html
# Linux
xdg-open /tmp/paperview_chart.html

For 3D charts (gl3d toolset), add this additional script tag before the main script:

<script src="https://cdn.jsdelivr.net/npm/echarts-gl@2/dist/echarts-gl.min.js"></script>

Render Word Cloud

When you receive word frequency data from /api/wordcloud/extract/, create an HTML file using echarts-wordcloud:

<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>PaperView Word Cloud</title>
  <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script>
  <script src="https://cdn.jsdelivr.net/npm/echarts-wordcloud@2/dist/echarts-wordcloud.min.js"></script>
  <style>body{margin:0;background:#fff}#chart{width:100vw;height:100vh}</style>
</head>
<body>
  <div id="chart"></div>
  <script>
    var chart = echarts.init(document.getElementById('chart'));
    var words = {{WORDCLOUD_DATA_JSON}};
    var clusterColors = ['#5470c6','#91cc75','#fac858','#ee6666','#73c0de'];
    chart.setOption({
      series: [{
        type: 'wordCloud',
        sizeRange: [14, 80],
        rotationRange: [-45, 45],
        gridSize: 8,
        shape: 'circle',
        textStyle: {
          fontFamily: 'sans-serif',
          color: function(params) {
            return clusterColors[params.data.cluster || 0];
          }
        },
        data: words.map(function(w) {
          return { name: w.word, value: w.frequency, cluster: w.cluster };
        })
      }]
    });
  </script>
</body>
</html>

Replace {{WORDCLOUD_DATA_JSON}} with the data array from the API response.

Render AI Diagram

The AI diagram endpoint already returns an image_url (CDN link). Simply download it or open in browser:

# Open in browser
open "https://cdn.example.com/generated/image.jpg"
# Or download
curl -o /tmp/paperview_diagram.png "https://cdn.example.com/generated/image.jpg"

Privacy & Data Handling

What data is sent to api.ipaperview.com:

  • ECharts API: The raw text data (CSV/JSON/text string) you provide in the data field is sent to the server. No local files are uploaded — you must pass the data content as a string in the request body.
  • AI Diagram API: Only the pdf_url (a public URL) is sent. The server fetches the PDF from that URL. No local files are uploaded from your machine.
  • Word Cloud API: Only the pdf_url (a public URL) is sent. The server fetches the PDF from that URL. No local files are uploaded from your machine.

Data retention:

  • Submitted text data (CSV/JSON) is processed in memory and not stored after the response is returned.
  • Generated AI diagram images are uploaded to Upyun CDN and retained for 30 days, after which they are automatically deleted.
  • No user data is sold or shared with third parties.

Authentication:

  • This skill requires a PAPERVIEW_API_TOKEN environment variable (format: pv_live_<hex_string>).
  • Obtain your token from www.ipaperview.com → Profile → API Token.
  • Each API call is authenticated and rate-limited per your subscription plan.

Usage Tips

  1. Auto-selection: For ECharts, omit toolset and template to let AI pick the best chart type for your data
  2. Large datasets: Safe to send large CSV files — AI only sees a sample, full data is processed server-side
  3. SSE parsing: AI diagram endpoint uses Server-Sent Events. Parse each data: line as JSON and wait for the type: "complete" event
  4. Arxiv URLs: Both http://arxiv.org/abs/... and https://arxiv.org/pdf/... formats are supported for pdf_url
  5. Custom styles: Use the custom_prompt and context fields to request specific colors, themes, or visual elements
  6. Always render: After receiving API results, always render them visually using the HTML templates above so the user can see the chart/wordcloud

平台分布

OpenClaw

96.26%
按下载量换算1,840

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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