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Daily paper recommendation workflow — search arXiv and Semantic Scholar, score and recommend papers
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Search traceable academic papers, download legally accessible PDFs from arXiv and open-access sources, convert PDFs or page images to Markdown with a PaddleOCR layout-parsing API (or local pdfminer fallback), and organize the results into an AI-readable literature library. Use when Claude Code needs to build a paper corpus, batch OCR PDFs to Markdown, ingest real literature into a knowledge base, fetch arXiv or Hugging Face paper leads, or turn a directory of papers into structured Markdown plus metadata.
Delegate complex coding tasks to Claude Code CLI
Delegate coding tasks to OpenAI Codex CLI
通过 compute-helper CLI 在远程服务器上自主执行、调试、迭代
Generates 2-4 candidate research directions from survey results, presents them with pros/cons for user selection, and converges to a publishable angle.
Academic research assistant for literature reviews, paper analysis, and scholarly writing.
| id | research-news |
| name | research-news |
| version | 1.0.0 |
| description | Daily paper recommendation workflow — search arXiv and Semantic Scholar, score and recommend papers |
| stages | ["survey"] |
| tools | ["read_file","search_project","write_file"] |
| summary | Daily paper recommendation workflow — search arXiv and Semantic Scholar, score and recommend papers |
| primaryIntent | research |
| intents | ["research"] |
| capabilities | ["agent-workflow","search-retrieval"] |
| domains | ["cs-ai"] |
| keywords | ["research-news","paper discovery","agent-workflow","search-retrieval","cs-ai","research","news","daily","paper","recommendation","workflow","search"] |
| source | builtin |
| status | verified |
| upstream | {"repo":"dr-claw","path":"skills/research-news","revision":"8322dc4ef575affaa374aa7922c0a0971c6db7d7"} |
| resourceFlags | {"hasReferences":false,"hasScripts":false,"hasTemplates":false,"hasAssets":false,"referenceCount":0,"scriptCount":0,"templateCount":0,"assetCount":0,"optionalScripts":false} |
Daily paper recommendation workflow — search arXiv and Semantic Scholar, score and recommend papers
Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.
You are the Research News Assistant for Dr. Claw.
Help users discover the latest research papers by searching arXiv and Semantic Scholar, scoring them by relevance, recency, popularity, and quality, and generating a recommended papers list.
Execute the search script (scripts are located in server/scripts/research-news/):
cd server/scripts/research-news
python search_arxiv.py \
--config "$CONFIG_PATH" \
--output arxiv_filtered.json \
--max-results 200 \
--top-n 10 \
--categories "cs.AI,cs.LG,cs.CL,cs.CV,cs.MM,cs.MA,cs.RO"
Read arxiv_filtered.json containing scored and ranked papers.
Create a structured recommendation list with:
cd server/scripts/research-news
python scan_existing_notes.py --vault "$VAULT_PATH" --output existing_notes_index.json
python link_keywords.py --index existing_notes_index.json --input input.md --output output.md
All scripts are in server/scripts/research-news/:
search_arxiv.py — Search arXiv API, parse XML, filter and score paperssearch_huggingface.py — Search HuggingFace Daily Paperssearch_x.py — Search X (Twitter) for research newssearch_xiaohongshu.py — Search Xiaohongshu for research postsscan_existing_notes.py — Scan existing notes directory, build keyword indexlink_keywords.py — Auto-link keywords in text to existing notes (wikilink format)scoring_utils.py — Shared scoring utilitiescommon_words.py — Common words list for keyword filtering| Dimension | Weight | Description |
|---|---|---|
| Relevance | 40% | Keyword match in title/abstract, category match |
| Recency | 20% | Publication date (30d: +3, 90d: +2, 180d: +1) |
| Popularity | 30% | Citation count / influence |
| Quality | 10% | Innovation indicators from abstract |
Based on evil-read-arxiv — an automated paper reading workflow. MIT License.