sci-trending-research

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Atualizado26 de abril de 2026 às 20:03

Research what's trending in science — emerging topics, publication surges, community discussions on academic Reddit (r/science, r/bioinformatics, r/MachineLearning, etc.), science Twitter/X, preprint servers, and the web. Surface real discussions, breakthroughs, and debates scientists are talking about right now. Produces a research brief other sci-* skills can consume. Triggers on: "what's trending in", "emerging research", "hot topics in", "recent breakthroughs", "what are researchers saying about", "field pulse", "research trends", "community sentiment on". Does NOT trigger for literature search (use sci-literature-research), data analysis, or manuscript writing.

Instalação

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.

Fonte

krmdel

krmdel/organon

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Bioquímicos e biofísicosCiências da Vida, Físicas e Sociais·SOC 19-1021

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name

sci-trending-research

description

Trending Research

Adapted from last30days by Ronnie-Nutrition. Original skill focused on research + prompt generation. This version strips the prompt layer and focuses purely on research synthesis — designed as a backend that other sci-* skills consume.

Outcome

A research brief saved to projects/sci-trending-research/{topic}_{YYYY-MM-DD}.md containing:

What researchers are actually discussing, publishing, and debating right now
Engagement-weighted insights (upvotes, likes, citations signal what resonates)
Patterns across platforms (strongest signals appear everywhere)
Actionable takeaways for research direction and hypothesis generation

Other skills (sci-hypothesis, sci-writing, sci-communication, etc.) can read the latest research brief for timely, relevant context.

Context Needs

File	Load level	How it shapes this skill
`research_context/research-profile.md`	Field + interests	Helps frame research through the scientist's domain lens
`context/learnings.md`	`## sci-trending-research` section	Apply previous feedback

Load if they exist. Proceed without them if not.

Before You Start

Check projects/sci-trending-research/ for recent research on the same topic. If a brief exists from the last 7 days, show the user: "I researched [topic] on [date]. Want to use that, refresh it, or research something new?"
Check API keys. Read .env for OPENAI_API_KEY and XAI_API_KEY. If either is missing, tell the user once before starting:
- Both missing: "I'll use web search for this research. For much richer results with real engagement data (upvotes, likes, comments), add OPENAI_API_KEY (for Reddit — get one at platform.openai.com) and XAI_API_KEY (for X — get one at console.x.ai) to your .env file."
- Only OpenAI missing: "I have X data but not Reddit. Add OPENAI_API_KEY to .env for Reddit threads with real upvotes and comments."
- Only xAI missing: "I have Reddit data but not X. Add XAI_API_KEY to .env for X posts with real likes and reposts."
- Both present: Skip this — say nothing, just proceed.
This is informational only. Never block work because keys are missing.

Step 1: Parse the Request

Extract from the user's input:

TOPIC — what scientific area or question they want to research
QUERY TYPE — what kind of research:
- BREAKTHROUGHS — "recent breakthroughs in X", "new discoveries in X" → wants key findings and developments
- METHODS — "new techniques for X", "emerging methods in X" → wants trending tools, protocols, techniques
- DEBATES — "controversies in X", "what's the debate about X" → wants disagreements and open questions
- GENERAL — anything else → wants broad understanding of community sentiment and emerging directions
SCOPE — quick (5-8 searches) or deep (12-18 searches). Default: balanced (8-12).

If the topic is vague, ask one clarifying question. Don't over-ask — get moving.

Step 2: Run the Research

Read references/research-methodology.md for the full search strategy.

Primary: Python Script (requires API keys)

The scripts/last30days.py script uses external APIs to search Reddit and X with real engagement data:

python3 .claude/skills/sci-trending-research/scripts/last30days.py "{topic}" --emit=compact

Reddit via OpenAI Responses API (web_search tool, domain-locked to reddit.com) — returns threads with real upvotes, comments, and top comment insights
X / Twitter via xAI API (x_search tool) — returns posts with real likes, reposts, and reply counts
Supports --quick (fewer sources) and --deep (comprehensive) flags
Supports --sources=reddit|x|both|auto to control which platforms to search
Supports --include-web to add general web search alongside Reddit/X

Requires: OPENAI_API_KEY (for Reddit) and/or XAI_API_KEY (for X) in .env. Script auto-detects available keys and adapts.

Fallback: WebSearch (no API keys needed)

If neither API key is configured, use Claude's built-in WebSearch:

Reddit (academic community discussions)

Search for: {topic} site:reddit.com targeting academic subreddits (r/science, r/bioinformatics, r/MachineLearning, etc.).

X / Twitter (science community pulse)

Search for: {topic} site:x.com OR site:twitter.com targeting science accounts and discussions.

Preprints and publications

Search for: {topic} site:biorxiv.org, {topic} site:arxiv.org, {topic} site:pubmed.ncbi.nlm.nih.gov.

Science news and journals

Search for: {topic} site:nature.com, {topic} site:sciencedaily.com, {topic} site:science.org.

Web (blogs, docs, news)

Search for: {topic} with time-filtered queries. Exclude reddit.com and x.com.

WebSearch works but lacks real engagement metrics (upvotes, likes). The script provides much richer data.

Step 3: Synthesize Findings

Read references/synthesis-guide.md for the full methodology.

Weight sources by engagement signals:

Reddit threads with 50+ upvotes and active discussion = strong signal
X posts with high engagement (likes, reposts) from researchers = trending signal
Preprints with significant social media attention = emerging signal
Published papers with editorial/news coverage = validated signal
Multiple sources saying the same thing = strongest signal

Synthesize by query type:

BREAKTHROUGHS → Extract specific findings, methods, and their significance:

Key breakthroughs:
1. [Discovery/finding] — [lab/group], [journal/preprint]
   - Community reaction: [sentiment from Reddit/X]
2. [Discovery/finding] — [lab/group], [source]

METHODS → Top techniques, tools, protocols ranked by adoption and discussion:

Trending methods:
1. [Technique] — discussed in 4 sources, gaining traction in [subfields]
   - Why: [what problem it solves]
   - Caveat: [limitations noted by community]

DEBATES → Key disagreements, open questions, competing hypotheses

GENERAL → Key themes, community sentiment, emerging directions

Step 4: Show Results

Display the synthesis in this format:

## What I found — {TOPIC} (last 30 days)

[2-4 sentence synthesis of the key insight]

### Key findings
1. [Finding with source attribution]
2. [Finding with source attribution]
3. [Finding with source attribution]

### Sources scanned
- Reddit: {n} threads across r/{sub1}, r/{sub2}
- X: {n} posts from @{handle1}, @{handle2}
- Preprints/Papers: {n} from bioRxiv, arXiv, PubMed
- Web: {n} pages from {domain1}, {domain2}

Step 5: Save the Brief

Save to projects/sci-trending-research/{topic-slug}_{YYYY-MM-DD}.md.

The brief format is defined in references/brief-template.md. Include:

Research metadata (topic, date, query type, sources scanned)
Synthesis (findings, patterns, emerging directions)
Raw source list (URLs, engagement metrics where available)
Research implications (how this research could inform next steps)

This file is what other skills consume. Keep it structured and scannable.

Step 6: Offer Next Steps

Based on the research and installed skills, recommend one action:

"This could inform a new hypothesis — want me to generate testable hypotheses with sci-hypothesis?"
"There's enough convergence here for a review or perspective piece — want me to draft a section with sci-writing?"
"This would make a good tutorial or explainer — want me to route to sci-communication?"
"I found [X] trending hard — want me to research deeper on that angle?"

Rules

Updated automatically when the user flags issues. Read before every run.

Self-Update

If the user flags an issue — bad sources, irrelevant results, wrong synthesis — update the ## Rules section immediately with the correction and today's date.

Troubleshooting

Too few results: Broaden the search terms. Strip modifiers and search for the core noun. Try --deep flag. Results feel outdated: Add year to search queries. Use "2026" or "this month" qualifiers. Platform-specific content missing: Some topics are discussed more on Reddit vs X. Use --sources=reddit or --sources=x to focus. Preprint-heavy topics: Add explicit bioRxiv/arXiv searches. Some fields discuss primarily on preprint servers. User wants real engagement metrics: Use the Python script with API keys. WebSearch fallback lacks exact counts. Script errors: Check .env has valid OPENAI_API_KEY and/or XAI_API_KEY. Fall back to WebSearch if scripts fail.