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Agent Skill · 研究检索

reading-agent(阅读 Agent)

reading-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

reading-agent 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,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 71,089。

总安装 196周安装 8GitHub Stars 4下载 63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
unknown

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:reading-agent(阅读 Agent)
来源仓库:https://github.com/nealcaren/sociology-skillset
仓库路径:skills/reading-agent
安装命令:
npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-agent
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-agent

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。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。支持根据关键词、任务场景或来源线索进行信息检索,适用于研究检索类需求。通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限和维护状态。

使用前建议核实是否会触发联网、命令执行或文件读写等操作。可结合原始 README 和仓库路径进一步核验具体用法和功能边界。

来源仓库内路径:skills/reading-agent

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-a…;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

    它需要执行命令,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

reading-agent 怎么安装?

在终端执行 npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

reading-agent 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

reading-agent 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

reading-agent 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

reading-agent 用的人多吗?

公开数据是累计安装 196 次,近一周安装 8 次,下载 63 次,GitHub 4 星。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。

reading-agent 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
reading-agent

阅读 Agent · 本页

1964执行命令未明确通过

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

Reading Agent

Create structured reading notes for academic papers. This skill can operate standalone or integrate with lit-synthesis.

Project Integration

This skill reads from project.yaml when available:

# From project.yaml
paths:
  lit_synthesis: literature/synthesis/

Project type: This skill works for all project types. Reading and analyzing literature is essential for qualitative, quantitative, and mixed methods research.

Saves reading notes to literature/synthesis/reading-notes/ by default, or to the path specified in project.yaml.

File Management

This skill uses git to track progress. After creating or updating reading notes:

  1. Stage and commit: git add [files] && git commit -m "reading-agent: Notes for [author-year]"

Do NOT create version-suffixed copies (e.g., -v2, -final, -working). The git history serves as the version trail.

When to Use This Skill

Use this skill when you need to:

  • Create reading notes for a single paper
  • Batch-process multiple PDFs into structured notes
  • Extract consistent metadata and analysis across papers
  • Prepare papers for literature synthesis

Quick Start

/reading-agent

I need to read: /path/to/paper.pdf

Or with a citation key:

/reading-agent

Citation key: smithCulturalFrames2020

Workflow

Step 0: Identify the Paper

Ask the user for:

  1. Paper source: PDF path, citation key (for local library lookup), or pasted text
  2. Identifiers (at least one, prefer citation key):

- Citation key (e.g., smithCulturalFrames2020) — from lit-search database or references.bib - OpenAlex ID (e.g., W2123456789) - DOI (e.g., 10.1086/123456)

Step 1: Access Paper Content

If PDF or EPUB provided (new paper): Ingest into the local library (converts to markdown + adds metadata to references.bib):

uv run plugins/sociology-skillset/scripts/ingest.py --file "/path/to/paper.pdf"
# Copies to library/pdfs/, converts to library/markdown/ (docling for PDFs, pandoc for EPUBs), appends to references.bib

If PDF provided for a paper already in references.bib (e.g., from lit-search but PDF was missing):

uv run plugins/sociology-skillset/scripts/ingest.py --file "/path/to/paper.pdf" --citekey Thompson2022_Inequality --update
# Converts the PDF, copies files to library/, updates the existing bib entry with pdf_path and md_path

If citation key provided: Look up the key in references.bib to find the md_path field, then read the markdown file at that path directly.

If markdown/text: Use directly.

Step 2: Read and Extract

For single-paper mode (default): Read the paper directly and create structured notes.

For batch mode: Spawn haiku agents using the Task tool:

Task tool parameters:
- subagent_type: "general-purpose"
- model: "haiku"
- prompt: [reading prompt with content and identifiers]
- description: "Read paper: AuthorYear"

Step 3: Save Notes

Save reading notes to: reading-notes/author-year-slug.md

The filename uses author-year-slug for readability, but frontmatter identifiers are the authoritative match back to source papers.


Reading Note Template

All reading notes must follow this structure with required frontmatter:

---
citation_key: smithCulturalFrames2020  # From references.bib or lit-search database (preferred)
openalex_id: W2123456789    # From lit-search database
doi: 10.1086/123456         # Digital Object Identifier
first_author: Smith
year: 2020
short_title: cultural-frames
---

# Smith 2020 - Cultural Frames

## Bibliographic Info
- **Authors**: [all authors]
- **Year**: [publication year]
- **Title**: [full title]
- **Journal**: [venue/publisher]
- **DOI**: [as link if available]
- **OpenAlex**: https://openalex.org/W2123456789

## Core Argument
[1-2 sentences: What is the paper arguing?]

## Theoretical Framework
- **Tradition**: [e.g., Bourdieusian, institutionalist, interactionist, framing theory]
- **Key concepts**: [list main theoretical concepts used]
- **Theory deployment**: [description | extension | critique | synthesis]

## Empirical Strategy
- **Data**: [what kind of data - interviews, content analysis, survey, etc.]
- **Methods**: [how data was analyzed]
- **Sample**: [who/what was studied, N if applicable]

## Key Findings
1. [Finding 1]
2. [Finding 2]
3. [Finding 3]

## Contribution Claim
[What does the paper claim to contribute to the literature?]

## Limitations
[As noted by the authors]

## Key Quotes
> "[Quote 1 - captures core argument or key concept]" (p. X)

> "[Quote 2 - methodological or theoretical insight]" (p. Y)

> "[Quote 3 - finding or implication]" (p. Z)

## Connections
[What other papers, concepts, or debates does this connect to?]

## Analytical Notes
[Your observations: How is the argument constructed? What assumptions underlie it? How does this relate to the broader project?]

Batch Processing Mode

For multiple papers:

  1. Gather papers: List of PDFs, EPUBs, or citation keys
  2. Ingest all files: Run uv run plugins/sociology-skillset/scripts/ingest.py --file <pdf> on each
  3. Prepare identifiers: From lit-search database or references.bib
  4. Spawn parallel agents: Multiple haiku agents via Task tool
  5. Collect outputs: Save to reading-notes/ directory

Example batch invocation:

/reading-agent

Batch process these papers:
- /papers/smith2020.pdf (DOI: 10.1086/123456)
- /papers/jones2019.pdf (OpenAlex: W2123456789)
- Citation key: brownMovementSpillover2021

The skill will spawn haiku agents in parallel for cost-effective processing.


Haiku Agent Prompt

When spawning agents for batch processing, use this prompt template:

You are a research assistant creating structured reading notes for a sociology literature synthesis project.

## Paper Identifiers
- Citation Key: {citation_key}
- OpenAlex ID: {openalex_id}
- DOI: {doi}
- PDF source: {pdf_path}

## Paper Content
{markdown_content}

## Instructions

Create reading notes following the exact template structure provided. Include:
1. YAML frontmatter with all available identifiers
2. All sections from Bibliographic Info through Analytical Notes
3. At least 3 key quotes with page numbers
4. Specific, observable details rather than vague summaries

Focus on:
- What the paper ARGUES, not just what it studies
- How theory is DEPLOYED, not just what theory is mentioned
- TENSIONS or gaps the paper leaves unresolved

Scripts

PDF/EPUB Conversion

Always use ingest.py to add papers — this ensures the PDF, markdown, and references.bib metadata stay linked via the citation key:

uv run plugins/sociology-skillset/scripts/ingest.py --file "/path/to/paper.pdf"

Uses docling for PDFs (with --image-export-mode placeholder) and pandoc for EPUBs. Files are organized as library/pdfs/{citekey}.pdf and library/markdown/{citekey}.md.

Setup

Install dependencies:

pip install docling   # for PDF conversion
brew install pandoc   # for EPUB conversion (or apt-get install pandoc)

Integration with Other Skills

From lit-search: Use the database.json to get OpenAlex IDs and DOIs for papers you've identified.

Finding related papers: Use semantic search to discover related papers in your library:

uv run plugins/sociology-skillset/scripts/rag.py search "topic from the paper you just read"

To lit-synthesis: Reading notes feed directly into:

  • Phase 2: Theoretical mapping (uses Tradition and Key concepts)
  • Phase 3: Thematic clustering (uses Findings and Methods)
  • Phase 4: Debate mapping (uses Connections and Analytical Notes)

With argument-builder: Key quotes and contribution claims inform prose drafting.


Model Recommendations

ModeModelRationale
Single paper (deep)OpusNuanced analysis, theoretical depth
Batch processingHaikuCost-effective structured extraction

Use Opus when you need to understand how an argument is constructed. Use Haiku when you need consistent extraction across many papers.


Output Directory Structure

reading-notes/
├── smith2020-cultural-frames.md
├── jones2019-institutional-logics.md
├── brown2021-movement-spillover.md
└── ...

Each file contains full structured notes with frontmatter identifiers for reliable cross-referencing.


Starting the Reading

When the user invokes this skill:

  1. Identify source: "What paper do you want to read? Provide a PDF path, citation key (for local library lookup), or paste the text."
  2. Gather identifiers: "What identifiers do you have? I need at least one of: citation key, OpenAlex ID, or DOI."
  3. Choose mode (if multiple papers): "Single paper or batch? For batch, I'll spawn haiku agents to process in parallel."
  4. Confirm output location: "I'll save reading notes to reading-notes/. Does that work, or do you want a different location?"
  5. Begin reading: Convert PDF if needed, then extract structured notes.

Key Reminders

  • Identifiers are essential: Every note needs at least one unique identifier in frontmatter (prefer citation_key)
  • Quotes need page numbers: Include (p. X) for every quote
  • Arguments over summaries: Capture what the paper argues, not just what it describes
  • Tensions are valuable: Note unresolved questions and potential gaps
  • Consistency enables synthesis: Following the template exactly allows cross-paper analysis

平台分布

Codex

38%
按下载量换算24

Claude

28.38%
按下载量换算18

Cursor

18.97%
按下载量换算12

Gemini CLI

9.4%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/nealcaren/sociology-skillset --skill reading-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

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