Token导航 LogoToken导航

Agent Skill · 研究检索

real-literature-trace(真实的文学痕迹)

real-literature-trace 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。

real-literature-trace 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,权限为只读;许可证信息还不明确,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 7,268。

总安装 19,472周安装 797GitHub Stars 73下载 8,741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:real-literature-trace(真实的文学痕迹)
来源仓库:https://github.com/ligphidonk/academic-skills
仓库路径:skills/real-literature-trace
安装命令:
npx skills add https://github.com/ligphidonk/academic-skills --skill 'Real Literature Trace'
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/ligphidonk/academic-skills --skill 'Real Literature Trace'

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。

用于学术文献的查找与追踪。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。支持按主题、作者或关键词检索相关研究成果。适合在研究初期快速获取领域背景和关键论文。使用时应结合具体需求验证来源可靠性。

real-literature-trace 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/real-literature-trace

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/ligphidonk/academic-skills --skill 'Real Liter…;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

real-literature-trace 怎么安装?

在终端执行 npx skills add https://github.com/ligphidonk/academic-skills --skill 'Real Literature Trac…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

real-literature-trace 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。

real-literature-trace 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。

real-literature-trace 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。

real-literature-trace 用的人多吗?

公开数据是累计安装 19,472 次,近一周安装 797 次,下载 8,741 次,GitHub 73 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。

real-literature-trace 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 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

  • 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 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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
real-literature-trace

真实的文学痕迹 · 本页

19,47273只读未明确有提醒

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

Real Literature Trace

Use this skill to turn a vague literature request into a traceable paper set with clear selection reasons and real source links.

What this skill is for

Use it when the user wants any of the following:

  • find real academic papers instead of invented references
  • return paper addresses that can be checked by the user
  • screen out weak, duplicate, or off-topic papers
  • build a core-paper list for a literature review
  • collect recent papers with publication traces
  • mix Chinese and English literature and keep the provenance clear
  • return CNKI links when possible, and otherwise give DOI, publisher, or other canonical pages
  • find strong international papers from Google Scholar, top journals, and top conferences

International source strategy

When the user wants foreign literature or a mixed Chinese-international set, treat the workflow as two linked passes:

  1. Discovery pass

- use Google Scholar-style discovery or equivalent search results to surface candidate papers - cast a wider net across journals, conferences, and preprints if needed

  1. Verification pass

- verify each selected paper on the publisher page, DOI page, or official proceedings page - prefer a canonical landing page over a search-result page whenever possible

When ranking international papers, prioritize these sources first:

  • top journals in the domain
  • top conferences in the domain
  • publisher pages with DOI or proceedings metadata
  • stable institutional repository pages only when the publisher page is unavailable

Do not treat a paper as strong just because it appears in Google Scholar. Use Scholar for discovery, then verify with an official source.

Default assumptions

If the user does not specify these details, use sensible defaults and state them briefly before searching:

  • focus on the last 5 years unless the topic is classical or the user asks for a longer range
  • aim for 12-18 papers for a normal literature set
  • prefer traceable sources over a larger but weaker set
  • prefer CNKI-reachable papers when the topic is Chinese or the user wants Chinese references
  • prefer top journals and top conferences when the user wants foreign or mixed literature
  • keep the final response in Chinese unless the user asks for another language

Scope narrowing

Do not jump straight into broad search if the topic is vague.

If the user says things like:

  • 帮我找文献
  • 搜一些论文
  • 做文献综述
  • 筛选优秀文献
  • 给我真实文献地址
  • a broad topic without task, scenario, or time window

then narrow the request first.

Ask only the highest-value missing details:

  • exact subtopic or application direction
  • Chinese, English, or mixed literature
  • recent papers only or classic + recent
  • how many papers they want
  • whether CNKI traceability is mandatory

If the user still stays vague after one round, choose one reasonable interpretation, say it explicitly, and proceed.

Search workflow

Follow this order.

1. Restate the working scope

Before searching, restate the agreed scope in 2-5 lines:

  • topic boundary
  • time window
  • source preference
  • target paper count
  • output format

2. Build a retrieval plan

Prepare a small bilingual keyword set:

  • 3-6 Chinese keyword groups
  • 3-6 English keyword groups
  • a few combinations that reflect task, method, dataset, or scenario

Split broad topics into subthemes so the results are diverse rather than repetitive.

If the user wants foreign literature, include venue-aware retrieval terms such as:

  • conference names
  • journal family names
  • method keywords
  • task keywords

Examples:

  • spacecraft pursuit evasion game IEEE TAC
  • orbital pursuit evasion game AIAA
  • differential game spacecraft Automatica

3. Search with traceability in mind

Prefer sources that can be verified later:

  • Crossref, DOI pages, publisher pages, or official proceedings pages for international papers
  • CNKI or CNKI-reachable records for Chinese papers
  • Google Scholar or scholar-style fallback only when needed for discovery, not for final quality decisions

For international screening, treat venue quality as part of the retrieval plan:

  • prefer papers from top journals or top conferences in the field
  • separate journal papers from conference papers in the notes
  • flag preprints as provisional unless a final published version is available

Do not promote a paper to the final set unless you can explain where it came from and how to check it.

4. Screen the candidate set

Keep papers that best satisfy most of these conditions:

  • strongly relevant to the narrowed topic
  • published in the target time window
  • methodologically representative or influential
  • from a credible venue
  • for foreign literature, from a recognized journal or conference with a stable official page
  • useful for a literature review, comparison table, or related-work section
  • traceable to a real page or record

Reject papers that are:

  • duplicates or near-duplicates
  • too generic to support the target topic
  • missing enough metadata to verify
  • low quality or clearly tangential

5. Verify links

For each selected paper, return the best available address in this order:

  1. canonical publisher or DOI page
  2. CNKI record or CNKI landing page when available
  3. other stable official page
  4. Google Scholar only as a discovery reference if no official page can be confirmed

If a link cannot be confirmed, say 需确认 instead of guessing.

6. Produce the final set

Return the final papers with:

  • title
  • authors
  • year
  • venue
  • why it was selected
  • paper status, such as strong, selected, or backup
  • canonical link
  • CNKI link if available
  • DOI if available
  • notes about any uncertainty

Screening rubric

Use a practical ranking approach:

  • A for core papers that are highly relevant and easy to verify
  • B for good supporting papers
  • C for backup papers that may still be useful
  • S for standout international papers from top journals or top conferences that are both highly relevant and strongly influential

When deciding between similar papers, prefer the one with:

  • better topical fit
  • clearer method or contribution
  • stronger venue or more stable source
  • easier link verification
  • for foreign papers, stronger venue reputation and better official traceability

Do not overvalue citation counts alone. A newer but more exact paper can be better than a famous but generic one.

Output format

When the user asks for a paper list, use a table by default.

Recommended columns:

  • 序号
  • 文献题目
  • 作者
  • 年份
  • 期刊/会议
  • 选择理由
  • 质量等级
  • 真实地址
  • CNKI地址
  • DOI
  • 谷歌学术/Scholar
  • 备注

If the user wants a literature review or related-work section, summarize the final set into:

  • research theme
  • major methods
  • gaps or limitations
  • recommended citation order

Quality rules

  • Never fabricate paper titles, DOIs, CNKI links, or publisher pages.
  • Never hide uncertainty. Mark unverified links as unverified.
  • Prefer fewer papers with real traceability over many papers with weak provenance.
  • If the topic is broad, explain the narrowing choice before searching.
  • If the user wants Chinese literature, make CNKI traceability part of the selection logic.
  • If the user wants foreign literature, make venue quality and official provenance part of the selection logic.

Good response pattern

  1. Confirm the scope.
  2. Search with broad-to-narrow queries.
  3. Screen by relevance and credibility.
  4. Verify links.
  5. Return the final paper set with selection reasons and traceable addresses.

平台分布

Codex

35.58%
按下载量换算3,110

Claude

30.79%
按下载量换算2,691

Cursor

21.5%
按下载量换算1,879

Gemini CLI

9.98%
按下载量换算872

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。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。

继续浏览同类 Skills