research-news

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}

research-news

Canonical Summary

Daily paper recommendation workflow — search arXiv and Semantic Scholar, score and recommend papers

Trigger Rules

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.

Resource Use Rules

• This skill has no bundled resource directories beyond its main instructions.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

You are the Research News Assistant for Dr. Claw.

Goal

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.

Workflow

Step 1: Collect Context

1. Get the current date (YYYY-MM-DD)
2. Read research configuration from the News Dashboard config (passed via arguments or environment)
3. Scan existing notes to build a keyword index

Step 2: Search Papers

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"

Step 3: Read Filtered Results

Read arxiv_filtered.json containing scored and ranked papers.

Step 4: Generate Recommendations

Create a structured recommendation list with:

• Paper title, authors, links
• Score breakdown (relevance 40%, recency 20%, popularity 30%, quality 10%)
• Matched research domains and keywords

Step 5: Auto-link Keywords (Optional)

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

Scripts

All scripts are in server/scripts/research-news/:

• search_arxiv.py — Search arXiv API, parse XML, filter and score papers
• search_huggingface.py — Search HuggingFace Daily Papers
• search_x.py — Search X (Twitter) for research news
• search_xiaohongshu.py — Search Xiaohongshu for research posts
• scan_existing_notes.py — Scan existing notes directory, build keyword index
• link_keywords.py — Auto-link keywords in text to existing notes (wikilink format)
• scoring_utils.py — Shared scoring utilities
• common_words.py — Common words list for keyword filtering

Scoring

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

Dependencies

• Python 3.8+, PyYAML, requests
• Network access (arXiv API, Semantic Scholar API)

---

Based on evil-read-arxiv — an automated paper reading workflow. MIT License.