| name | social-media-paper-triage |
| description | Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.
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Social Media Paper Triage
Turn social media paper recommendations into actionable research items.
When to Use
- User forwards a 小红书 post about a paper
- User shares a WeChat公众号 article discussing papers
- User shares a Twitter/X thread about a paper or method
- User asks "find the original paper from this link"
- User shares any blog post / newsletter that references academic papers
Workflow
Step 1: Extract Content from Platform
Use platform-specific tools to fetch the full content:
| Platform | Tool | Command |
|---|
| 小红书 | Agent Reach (XiaoHongShu) | mcporter call 'xhs.get_note(note_url: "URL")' |
| WeChat公众号 | Agent Reach (WeChat) | python3 ~/.agent-reach/.venv/bin/wechat_article.py "URL" |
| Twitter/X | xreach | xreach tweet URL --json or xreach thread URL --json |
| Reddit | Agent Reach | mcporter call 'reddit.read_post(url: "URL")' |
| Bilibili | Agent Reach | mcporter call 'bilibili.get_video_info(url: "URL")' |
| Any URL | Jina Reader | curl -s "https://r.jina.ai/URL" |
Step 2: Identify Papers
From the extracted content, identify all referenced papers:
- Look for: paper titles, arXiv IDs, DOIs, author names + year citations
- Distinguish between: the main paper being discussed vs. papers cited in passing
- Note: social media posts often use informal titles or translated titles
Step 3: Find Original Sources
For each identified paper, find the authoritative source:
- arXiv search: Check if it's on arXiv (most ML/AI papers are)
- Semantic Scholar: Search by title for metadata + citation count
- Google Scholar (via web search): Fallback for non-arXiv papers
Priority: arXiv PDF > conference proceedings > journal version
Step 4: Summarize for Decision
Present a concise summary to the user:
📄 Paper: [Title]
👥 Authors: [First author] et al., [Year]
🏛 Venue: [Conference/Journal]
📊 Citations: [N]
🔗 Original: [arXiv/DOI link]
📱 Source: [social media link]
TL;DR: [2-3 sentence summary of what the paper does and why it matters]
Relevance to your work: [brief assessment based on user's research context]
Step 5: User Decision → Action
Based on user's response:
- "Add to reading queue" → Add to Zotero
30_Reading Queue with appropriate tag
- "Not relevant" → Done, no action
- "Read it now" → Switch to paper-reading skill
- "Save for later" → Add to Zotero with lower priority tag
Key Principles
- Always find the original paper — don't just summarize the social media post
- Don't auto-add to Zotero — summarize first, let user decide
- Preserve the social media link — add as a note/attachment in Zotero for provenance
- Assess relevance — use knowledge of user's active projects to judge fit
- Batch processing — if the post mentions multiple papers, triage all of them at once
Common Gotchas
- WeChat articles often translate paper titles to Chinese — search by original English title
- 小红书 posts may summarize methods inaccurately — always verify against the original
- Twitter threads may reference preprints that have been updated/published since
- Some posts discuss methods without naming specific papers — ask user for clarification
