Research

Topic & Trend Research for X Content

Research what's being discussed, debated, and shared in a specific niche to identify high-potential content opportunities.

Input

The user provides:

• A niche or topic to research (e.g., "Rust", "local LLMs", "developer tools")
• Or asks "what should I post about?"
• Or wants to understand what's trending

Process

Step 1 — Web Research

Search for current conversations and trends using WebSearch:

1. Search X/Twitter directly for the niche topic + "2026" to find recent discussions
2. Search Reddit (r/programming, niche subreddits) for hot topics
3. Search Hacker News for trending technical discussions
4. Search for recent releases/announcements in the niche
5. Search for controversies or debates (these drive highest engagement)

Use WebSearch with queries like:

• site:x.com [niche topic] 2026
• [niche] trending discussion March 2026
• [niche] controversial opinion 2026
• [niche] new release announcement 2026

Deep-read promising results using WebFetch to extract full content from the most relevant search results. This gives you the actual conversations, not just titles.

If chrome is available and you need to browse X's live feed for real-time conversations, you can navigate to X search:

• https://x.com/search?q=[topic]&f=live — latest posts on the topic
• Read the actual discussions, not just search snippets

Step 2 — Identify Content Opportunities

Categorize findings into opportunity types:

Hot Takes — Controversial discussions where you can add a unique perspective

• Look for: debates, strong opinions, "unpopular opinion" threads
• Signal: High reply counts indicate engagement potential

Tutorials — Questions people are asking that you can answer

• Look for: "How do I...", Stack Overflow trending, common mistakes
• Signal: Repeat questions indicate unmet demand

Announcements — New releases, updates, or changes you can contextualize

• Look for: GitHub releases, product launches, API changes
• Signal: Timing matters — first-mover advantage in commentary

Comparisons — Tools/approaches being debated

• Look for: "X vs Y", "switching from A to B", benchmark results
• Signal: People love definitive comparisons with data

Pain Points — Frustrations being expressed

• Look for: "I hate when...", "why can't...", bug reports, rants
• Signal: Pain point posts have extremely low negative signal risk

Build-in-Public — Trends in the making/building space

• Look for: What others are sharing about their progress
• Signal: Gaps in what's being shared that you could fill

Step 3 — Prioritize by Phoenix Score Potential

Rank opportunities by predicted engagement:

1. Conversation potential — Will this generate replies the author can respond to? (75–150× for author reply threads, 13–27× for replies)
2. Bookmark potential — Is this save-worthy reference material? (~10× weight)
3. Dwell potential — Will people spend time reading carefully? (high weight)
4. Timeliness — Is this trending NOW? (freshness bonus)
5. Authenticity fit — Can you write about this genuinely? (reduces negative risk)

Step 4 — Generate Content Briefs

For the top 5 opportunities, create a brief:

## Opportunity: [Title]

**Why now**: [What's happening that makes this timely]
**Angle**: [Your unique perspective or value-add]
**Format**: [Post / Thread / Poll / Quote tweet]
**Hook idea**: [Draft opening line]
**Key points**: [3-5 bullets of what to cover]
**Reply trigger**: [The question or take that will drive replies]
**Risk**: [Low / Medium — any negative signal concerns]

Step 5 — Output

Present:

1. Trend summary — What's happening in the niche right now (2-3 sentences)
2. Top 5 content opportunities — Ranked by predicted engagement
3. Content briefs — For each opportunity
4. Suggested posting schedule — When to publish each piece
5. Reply farming targets — 5-10 active discussions to engage with (builds visibility before posting)

Tips

• The best content comes from the intersection of what's trending + what you genuinely know
• "Reply farming" (engaging in relevant conversations) for 15-30 min before posting increases your visibility to the algorithm
• Time-sensitive opportunities should be published within 24-48 hours
• Evergreen opportunities can be queued for optimal timing

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Epistates
• Source: Epistates/sparX
• License: MIT

Install and usage instructions live in the source repository linked above.