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real-literature-trace真实的文学痕迹

Agent Skill

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。

总安装

19,472

周安装

797

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下载量

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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'

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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