Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jxtse/scientific-research-skills --skill social-media-paper-triagegit clone --depth 1 https://github.com/jxtse/scientific-research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jxtse/scientific-research-skills/social-media-paper-triage)<a href="https://agentmods.dev/skills/jxtse/scientific-research-skills/social-media-paper-triage"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/social-media-paper-triage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jxtse/scientific-research-skills/social-media-paper-triage"><img src="https://agentmods.dev/badge/skills/jxtse/scientific-research-skills/social-media-paper-triage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00167 | $0.01024 |
| Opus 5 | $0.00084 | $0.00512 |
| Sonnet 5 | $0.00033 | $0.00205 |
| Haiku 4.5 | $0.00017 | $0.00102 |
Grade A, and why
social-media-paper-triage scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| **Any URL** | Jina Reader | `curl -s "https://r.jina.ai/URL"` | How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
| 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]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 100 lines · 167 tokens per session scan A c25b1fcacd7f
social-media-paper-triage is a skill published in the GitHub repository jxtse/scientific-research-skills (68 stars, last pushed 3mo ago), licensed MIT. It adds 167 tokens to every session and 1,024 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
paper-fetch
Retrieve one specified academic paper (by title, arXiv ID, DOI, URL, or a local .md/.txt/.pdf path the caller already has) and land it on disk as source.md plus a source.meta.json carrying a line-number section index. Checks context/papers/ for an existing copy first; local files and direct PDF URLs are read directly…
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bias-detection
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
competitive-intelligence
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claim-analysis
Deep claim scope analysis — decompose independent/dependent claims and assess protection scope breadth. Budget: 30 patent families, 30 claim parses, 20 web searches.
adversarial-persona
Strategy: Role-play attacks from hostile personas — competing lab researcher, hostile reviewer, funding skeptic, domain outsider — each with distinct attack motivations and blind spots.