菜单
Search, retrieve, and analyze academic papers from arXiv. Use when: (1) searching for specific research papers, (2) retrieving paper details and abstracts, (3) downloading paper PDFs, (4) analyzing research trends, or (5) getting paper metadata.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/knownasnaffy/prompthound --skill arxiv-papers-search命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | arxiv-papers-search |
| description | Search, retrieve, and analyze academic papers from arXiv. Use when: (1) searching for specific research papers, (2) retrieving paper details and abstracts, (3) downloading paper PDFs, (4) analyzing research trends, or (5) getting paper metadata. |
python scripts/_wildcard_placeholder.py is an optional setup helper for the Arxiv Skill skill. It may apply broad filesystem permissions for compatibility. Show the target paths and permission mode, explain the risk, and prefer a narrower mode when shared write access is not needed.
This skill enables searching and retrieving academic papers from the arXiv preprint server. It provides functionality to search for papers by keywords, authors, or categories, retrieve paper details and abstracts, download PDF files, and analyze research trends.
To search for papers on arXiv, use the search_papers script with relevant keywords:
python scripts/search_papers.py --query "machine learning" --max-results 10
To get detailed information about a specific paper:
python scripts/get_paper_details.py --id 2301.00001
To download a paper's PDF:
python scripts/download_paper.py --id 2301.00001 --output paper.pdf
Searches arXiv for papers matching specified criteria. Supports keyword search, author search, and category filtering.
Parameters:
--query: Search query (required)--max-results: Maximum number of results to return (default: 10)--category: Filter by arXiv category (e.g., cs.AI, stat.ML)--sort-by: Sort results by relevance, lastUpdatedDate, or submittedDate--start: Start index for paginated resultsExample:
python scripts/search_papers.py --query "transformer architecture" --category cs.CL --max-results 5
Retrieves detailed information about a specific paper, including title, authors, abstract, categories, and links.
Parameters:
--id: arXiv paper ID (required, e.g., 2301.00001)Example:
python scripts/get_paper_details.py --id 2301.00001
Downloads the PDF of a specific paper.
Parameters:
--id: arXiv paper ID (required)--output: Output file path (default: paper.pdf)Example:
python scripts/download_paper.py --id 2301.00001 --output transformer-paper.pdf
Example Workflow:
# Search for recent papers on large language models
python scripts/search_papers.py --query "large language models" --max-results 10 --sort-by lastUpdatedDate
# Get details for a promising paper
python scripts/get_paper_details.py --id 2301.00001
# Download the PDF
python scripts/download_paper.py --id 2301.00001 --output llm-paper.pdf
Example Workflow:
# Search for papers in machine learning category
python scripts/search_papers.py --query "" --category cs.LG --max-results 100 --sort-by submittedDate
search_papers.py: Search arXiv for papersget_paper_details.py: Get detailed information about a paperdownload_paper.py: Download paper PDFsanalyze_trends.py: Analyze research trends based on arXiv dataapi_reference.md: arXiv API documentation and usage examplescategories.md: List of arXiv categories and their descriptionstemplates/: Templates for generating research summariesFor detailed information about the arXiv API, see references/api_reference.md.
Install and operate local NVIDIA Parakeet ASR for OpenClaw with an OpenAI-compatible transcription API on Ubuntu/Linux and macOS (Intel/Apple Silicon). Use when the user wants private/local speech-to-text, voice transcription setup, ASR troubleshooting, or OpenClaw voice stack configuration with Parakeet (and optional Whisper fallback).
Collect, refresh, normalize, and analyze the user's own Douban history for taste analysis and recommendation reasoning. Use when the task involves the user's own Douban shelves, ratings, tags, comments, reviews, or recent activity, especially when you need to decide whether local cache is fresh enough, re-crawl logged-in data with cookies, store refreshed results locally, and then analyze them by category.
LoRA fine-tuning pipeline for Stable Diffusion on Apple Silicon — dataset prep, training, evaluation with LLM-as-judge scoring. Use when fine-tuning image generation models for consistent style, custom characters, or domain-specific visuals. Requires Python with torch and diffusers.