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ppingzhang

论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download!

by ppingzhang · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install paper-search-and-download-automatically
Description
Search and download related arXiv papers by topic plus date range, or from a seed paper title/id. Use when user asks to crawl related papers, collect arXiv a...
Usage Guidance
This skill appears to do what it says: query the arXiv API and download PDFs into ./arxiv/. Before installing/using it, consider: (1) run it in a workspace where writing ./arxiv/ is acceptable and you have disk space; (2) avoid very large max-results values to respect arXiv rate limits and site policies; (3) review/confirm any seed-title you pass (title matching can be imprecise); (4) verify you trust the skill source since the repository owner is unknown even though the code is small and uses only the Python standard library; and (5) if you need corporate/network auditability, run it where network egress to arxiv.org is allowed and logged.
Capability Assessment
Purpose & Capability
The name/description (search and download arXiv papers) matches the included script and SKILL.md. The script constructs arXiv API queries, parses the Atom feed, and downloads PDFs—exactly what the skill claims to do. No unrelated services, binaries, or credentials are requested.
Instruction Scope
SKILL.md instructs the agent to run the included Python script from the workspace root with clearly defined arguments and to report counts and output path. The script only reads its CLI args, performs HTTP(S) requests to arXiv endpoints, parses XML, and writes files to ./arxiv/. It does not attempt to read other files, environment variables, or external endpoints.
Install Mechanism
No install specification is provided (instruction-only + an included script). The script uses only Python standard library modules; nothing is downloaded or executed during install. This is low-risk and proportionate for the functionality.
Credentials
The skill requires no environment variables, credentials, or config paths. All network calls are to arXiv endpoints (export.arxiv.org and arxiv.org PDFs) which is appropriate for the described purpose.
Persistence & Privilege
The skill does not request always:true and does not modify other skills or system-wide config. It writes PDFs to a local ./arxiv/ directory (as expected) and has normal autonomous-invocation defaults.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install paper-search-and-download-automatically
  3. After installation, invoke the skill by name or use /paper-search-and-download-automatically
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
This skill enables keyword-based academic paper retrieval, with optional constraints on time range and result count. The retrieved papers are downloaded and saved to a specified directory, helping users quickly access recent or representative research in target domains (e.g., large language models). Keyword-based Search: Supports retrieving papers using topic keywords such as “LLM”, “multimodal”, and “video understanding”. Time Filtering: Allows restricting papers by publication date, such as the last year, 2025–2026, or after 2024. Result Limit Control: Supports limiting the number of returned papers (e.g., 10 papers). Structured Output: Saves results to a specified directory in a standardized format for easy programmatic access and management. 该 Skill 支持基于关键词的学术论文检索,并结合时间范围与数量限制进行高效筛选,同时将结果下载并保存到指定目录,帮助用户快速获取目标领域(如大模型)的最新或代表性研究成果。 关键词检索:支持根据主题关键词检索相关论文,如“大模型”“多模态”“视频理解”等。 时间限制:支持限定论文发布时间范围,如近一年、2025–2026、2024年之后。 数量限制:支持限定返回论文数量,如10篇文章。 固定格式输出:支持将检索结果按照统一字段格式写入指定目录,便于程序化读取与管理。
v1.0.0
This skill enables keyword-based academic paper retrieval, with optional constraints on time range and result count. The retrieved papers are downloaded and saved to a specified directory, helping users quickly access recent or representative research in target domains (e.g., large language models). Keyword-based Search: Supports retrieving papers using topic keywords such as “LLM”, “multimodal”, and “video understanding”. Time Filtering: Allows restricting papers by publication date, such as the last year, 2025–2026, or after 2024. Result Limit Control: Supports limiting the number of returned papers (e.g., 10 papers). Structured Output: Saves results to a specified directory in a standardized format for easy programmatic access and management. 该 Skill 支持基于关键词的学术论文检索,并结合时间范围与数量限制进行高效筛选,同时将结果下载并保存到指定目录,帮助用户快速获取目标领域(如大模型)的最新或代表性研究成果。 关键词检索:支持根据主题关键词检索相关论文,如“大模型”“多模态”“视频理解”等。 时间限制:支持限定论文发布时间范围,如近一年、2025–2026、2024年之后。 数量限制:支持限定返回论文数量,如10篇文章。 固定格式输出:支持将检索结果按照统一字段格式写入指定目录,便于程序化读取与管理。
Metadata
Slug paper-search-and-download-automatically
Version 1.0.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download!?

Search and download related arXiv papers by topic plus date range, or from a seed paper title/id. Use when user asks to crawl related papers, collect arXiv a... It is an AI Agent Skill for Claude Code / OpenClaw, with 80 downloads so far.

How do I install 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download!?

Run "/install paper-search-and-download-automatically" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download! free?

Yes, 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download! is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download! support?

论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download! is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created 论文关键词搜索和自动下载到指定目录 Keyword search for papers and automatic download!?

It is built and maintained by ppingzhang (@ppingzhang); the current version is v1.0.1.

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