| name | str-trending-research |
| description | Research what's trending in the last 30 days across Reddit, X, and the web. Surface real discussions, recommendations, and patterns people are talking about right now. Produces a research brief other skills can consume. Triggers on: "research", "what's trending", "research topic", "research X", "what are people saying about", "trending in", "last 30 days", "recent discussions", "what's hot", "look into", "dig into", "find out what people think about", "what's new with", "community sentiment on". Does NOT trigger for brand voice, positioning, ICP, or content writing. Does NOT trigger for general web browsing or simple URL fetching.
|
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 skills consume.
Outcome
A research brief saved to projects/str-trending-research/{YYYY-MM-DD}_{topic}.md containing:
- What people are actually discussing, recommending, and debating right now
- Engagement-weighted insights (upvotes, likes, comments signal what resonates)
- Patterns across platforms (strongest signals appear everywhere)
- Actionable takeaways for content creation
Other skills (mkt-content-atomizer, email-sequences, etc.) can read the latest research brief for timely, relevant content.
Context Needs
| File | Load level | How it shapes this skill |
|---|
brand_context/icp.md | Language section | Helps frame research through the audience's lens |
context/learnings.md | ## str-trending-research section | Apply previous feedback |
Load if they exist. Proceed without them if not.
Before You Start
-
Check projects/str-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 they want to research
- QUERY TYPE — what kind of research:
- RECOMMENDATIONS — "best X", "top X", "what X should I use" → wants a ranked list of specific things
- NEWS — "what's happening with X", "X news", "latest on X" → wants current events
- HOW-TO — "how to X", "X techniques", "X best practices" → wants methods and approaches
- GENERAL — anything else → wants broad understanding of community sentiment
- 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/str-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 (community discussions)
Search for: {topic} site:reddit.com and related queries.
X / Twitter (real-time pulse)
Search for: {topic} site:x.com OR site:twitter.com and related queries.
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) = trending signal
- Blog posts from known authors/sites = authority signal
- Multiple sources saying the same thing = strongest signal
Synthesize by query type:
RECOMMENDATIONS → Extract specific names, count mentions, rank by popularity:
Most mentioned:
1. [Specific thing] — mentioned 5x (r/sub, @handle, blog.com)
2. [Specific thing] — mentioned 3x (sources)
NEWS → Timeline of events, key developments, community reactions
HOW-TO → Top techniques, common approaches, what's working vs what's not
GENERAL → Key themes, community sentiment, debates and consensus
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}
- Web: {n} pages from {domain1}, {domain2}
Step 5: Save the Brief
Save to projects/str-trending-research/{YYYY-MM-DD}_{topic-slug}.md.
The brief format is defined in references/brief-template.md. Include:
- Research metadata (topic, date, query type, sources scanned)
- Synthesis (findings, patterns, recommendations)
- Raw source list (URLs, engagement metrics where available)
- Suggested content angles (how this research could inform content)
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 would make a strong [LinkedIn post / YouTube video / email topic] — want me to draft something?"
- "I found [X] trending hard — want me to research deeper on that angle?"
- Route to the appropriate content skill if installed.
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.
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.
