Trend Analysis Skill

Frameworks and techniques for identifying emerging trends and turning them into content opportunities.

The Trend Lifecycle

          Discovery → Growth → Peak → Decline → Maturity
              ↑          ↑       ↑       ↑          ↑
            Early    Rising   Hot   Saturated  Evergreen
           Adopters  Stars   Topic   Takes     Reference

Phase Characteristics

Phase	Discussion	Competition	Opportunity
Discovery	Niche communities	Very low	Define the conversation
Growth	Expanding rapidly	Low-Medium	Build authority
Peak	Maximum volume	High	Need unique angle
Decline	Moving on	Decreasing	Contrarian takes
Maturity	Established	Low (new entrants)	Best practices

Trend Identification Signals

Early Stage Signals (High Value)

• Questions appearing in niche forums
• "What is X?" search patterns rising
• Early adopter communities buzzing
• First mainstream article appears
• Investors starting to mention

Growth Stage Signals

• Multiple articles/podcasts covering
• Debates about approach/implementation
• "How to X" content proliferating
• Job postings mentioning topic
• Conference sessions being added

Peak Stage Signals

• Everyone has an opinion
• Backlash articles appearing
• "Is X overhyped?" pieces
• Mainstream media coverage
• Parody/meme content

Decline Signals

• "X is dead" articles
• Conversation shifts to "what's next"
• New topic replacing it
• Only laggards still discovering
• Best practices established

Trend Evaluation Framework

TEMPO Analysis

T - Timing

• Where in the lifecycle?
• How long until peak?
• When is the optimal moment to publish?

E - Evidence

• What data supports this trend?
• How strong are the signals?
• Are sources credible?

M - Momentum

• Is discussion accelerating?
• What's the velocity of new content?
• Are influencers engaging?

P - Positioning

• How does user's expertise align?
• What unique angle is available?
• Can they add genuine value?

O - Opportunity

• What content formats fit?
• What's the competition like?
• What's the upside?

Scoring Matrix

Factor	Weight	Score (1-5)	Weighted
Timing	25%
Evidence	20%
Momentum	20%
Positioning	20%
Opportunity	15%
Total	100%

Interpretation:

• 4.0+ : Excellent opportunity, act now
• 3.0-4.0 : Good opportunity, plan content
• 2.0-3.0 : Monitor, may improve
• <2.0 : Pass or wait

Content Timing Strategy

Optimal Timing by Content Type

Content Type	Best Phase	Why
Explainer/101	Early Growth	Be the reference
Opinion/Take	Growth to Peak	Join the conversation
Deep Analysis	Peak	Differentiate from hot takes
Contrarian	Late Peak	Stand out from consensus
Best Practices	Decline/Maturity	Provide closure
Retrospective	Maturity	Historical perspective

Timing Considerations

• Speed vs. Quality: Early phases favor speed; peak favors quality
• Expertise Match: Only go early if you genuinely know the topic
• Audience Readiness: Are your followers aware of this trend?
• Platform Fit: LinkedIn may lag Twitter by days/weeks

Research Techniques

Search Pattern Variations

[topic] + trends
[topic] + what's changing
[topic] + controversy
[topic] + vs [alternative]
[topic] + dead
[topic] + future
[topic] + predictions 2026
[topic] + hot take
why [topic] matters
[topic] + problem
[topic] + mistake

Source Prioritization

1. Leading Indicators

   • Hacker News discussions
   • Substack newsletters
   • Early-adopter Twitter/X
   • Reddit niche communities

2. Confirming Indicators

   • Industry publications
   • Tech news sites
   • Mainstream business press
   • Conference agendas

3. Lagging Indicators

   • General news coverage
   • LinkedIn trending
   • "What is X" articles
   • Corporate announcements

Trend-to-Content Mapping

From Trend to Article Angle

Trend: AI code assistants changing developer workflows

Possible Angles:

1. "The hidden cost of AI-assisted coding" (contrarian)
2. "How I use [tool] without losing my skills" (personal/practical)
3. "5 tasks where AI assistants fail completely" (specific/surprising)
4. "The junior developer's guide to AI coding tools" (audience-specific)
5. "AI coding tools: 3 months later" (retrospective)

Angle Selection Criteria

• Uniqueness: Am I saying something new?
• Evidence: Can I back this up?
• Voice Match: Is this how I naturally think?
• Audience Value: Does my audience need this?
• Timeliness: Why now specifically?

Memory Integration for Trends

What to Store

Store in semantic memory:

• Trend name and current phase
• Key sources/evidence
• Optimal content angles
• Timing recommendations

Example:

Category: ["trends", "developer-tools"]
Content: "AI code assistant trend in growth phase (Jan 2026).
Evidence: HN discussions up 300% QoQ, GitHub Copilot surpassed
1M users. Best angles: productivity claims (contrarian),
skill development concerns, workflow integration tips.
Optimal timing: next 4-6 weeks before peak saturation."

What to Query

Before trend research:

• Past trends in this space
• User's expertise areas
• Previous content on related topics

Common Trend Research Mistakes

1. Confirmation Bias - Only looking for trends you want to find
2. Echo Chamber - Relying on single source/community
3. Recency Bias - Assuming new = trending
4. Trend Conflation - Combining separate trends into one
5. Peak Blindness - Missing that peak has passed
6. Evergreen Confusion - Calling persistent topics "trends"

Trend Report Template

# Trend Report: [Industry/Topic]
Generated: [Date]
Research Depth: [Quick/Standard/Deep]

## Executive Summary
[2-3 sentence overview of findings]

## Top Trends

### 1. [Trend Name]
- **Phase**: Discovery/Growth/Peak/Decline
- **TEMPO Score**: X.X/5.0
- **Evidence**: [Brief summary with sources]
- **Your Angle**: [Recommended approach]
- **Timing**: [When to act]
- **Content Recommendation**: [Specific suggestion]

### 2. [Trend Name]
[Same structure]

## Quick Opportunities
[Bullet list of immediate actions]

## Watch List
[Emerging topics to monitor]

## Not Worth Pursuing
[Trends that don't fit or are saturated]