Analyze trends over time by comparing current entity mentions against historical data from memory. Identifies topics gaining or losing popularity, emerging themes, and provides trend velocity and trajectory analysis for weekly trend reports.
Analyze trends over time by comparing current entity mentions against historical data from memory. Identifies topics gaining or losing popularity, emerging themes, and provides trend velocity and trajectory analysis for weekly trend reports.
license
MIT
compatibility
Requires Memory MCP server for historical data access
[{"name":"current_period_articles","type":"list[ProcessedArticle]","description":"Articles from the current analysis period"},{"name":"lookback_days","type":"int","default":14,"description":"Number of days to look back for historical comparison"},{"name":"min_mentions","type":"int","default":3,"description":"Minimum mentions for an entity to be considered trending"}]
output
[{"name":"trends","type":"list[TrendAnalysis]","description":"List of identified trends with velocity and trajectory"},{"name":"emerging_topics","type":"list[str]","description":"New topics not seen in historical period"},{"name":"declining_topics","type":"list[str]","description":"Topics significantly less mentioned than before"},{"name":"summary","type":"str","description":"Narrative summary of trend landscape"}]
Analyze Trends Historical
Compare current news coverage against historical data to identify trend trajectories and produce trend analysis reports.
Zero or minimal mentions (<2) in historical period
Not a one-time event (appears across multiple sources)
Step 6: Identify Declining Topics
Topics are declining if:
Significant mentions in historical period (>5)
Minimal or no mentions in current period
Not a concluded news story (e.g., not "X acquisition complete")
Step 7: Calculate Topic Clusters
Group related entities:
Entities that frequently co-occur
Entities with similar trajectory patterns
Create topic clusters with representative name
Step 8: Generate Trend Summary
Write a 2-3 paragraph narrative:
Top rising trends and what's driving them
Notable emerging topics to watch
Topics losing steam and possible reasons
Step 9: Rank and Prioritize
Sort trends by:
Absolute velocity (magnitude of change)
Current mention count (significance)
Importance score average (quality of coverage)
Output Schema
{"trends":[{"entity":"LoRA fine-tuning","current_mentions":15,"historical_mentions":5,"current_rate":2.14,"historical_rate":0.36,"velocity":4.94,"velocity_class":"surging","avg_importance":7.5,"sources":["TechCrunch","VentureBeat","Hugging Face Blog"],"related_entities":["PEFT","QLoRA","adapters"],"context":"Growing interest in parameter-efficient fine-tuning methods"}],"emerging_topics":["Claude 3.5 Opus","mixture-of-agents"],"declining_topics":["GPT-3.5","BERT"],"summary":"The AI landscape this week shows a strong shift toward efficient fine-tuning methods, with LoRA-related coverage surging 5x compared to the previous two weeks. This aligns with several new library releases making fine-tuning more accessible. Meanwhile, discussion of older architectures like BERT continues to decline as transformer-based LLMs dominate. Emerging topics include Claude 3.5 Opus and mixture-of-agents architectures, both appearing for the first time with multiple high-importance mentions."}
Success Criteria
Historical data queried successfully
Velocity calculated correctly for all current entities
{"trends":[{"entity":"LoRA","current_mentions":2,"historical_mentions":1,"velocity":1.0,"velocity_class":"rising","avg_importance":7.5,"sources":["TechCrunch","Hugging Face"],"related_entities":["PEFT","fine-tuning"],"context":"Increased coverage of LoRA fine-tuning methods"},{"entity":"Claude","current_mentions":1,"historical_mentions":1,"velocity":0.0,"velocity_class":"stable","avg_importance":9.0,"sources":["VentureBeat"],"related_entities":["Anthropic"],"context":"Steady coverage of Claude developments"}],"emerging_topics":["PEFT"],"declining_topics":["GPT-4"],"summary":"LoRA and parameter-efficient fine-tuning are seeing increased attention, with mentions doubling compared to the previous two weeks. This trend coincides with new library releases making these techniques more accessible. Claude maintains steady coverage with a high-importance update. GPT-4 discussion has notably declined, possibly as the community's attention shifts to newer developments."}
Example 2: Emerging Topic Detection
Input:
{"current_period_articles":[{"title":"Mixture of Agents: New Architecture","entities":["mixture-of-agents","MoA","ensemble"],"importance_score":9,"published_at":"2026-01-26"},{"title":"MoA Shows Promise","entities":["mixture-of-agents","benchmarks"],"importance_score":8,"published_at":"2026-01-25"}],"lookback_days":14,"min_mentions":2}
Memory Query Result (mocked):
{"historical_articles":[]}
Output:
{"trends":[{"entity":"mixture-of-agents","current_mentions":2,"historical_mentions":0,"velocity":null,"velocity_class":"emerging","avg_importance":8.5,"sources":["Multiple"],"related_entities":["MoA","ensemble"],"context":"New architectural approach appearing for first time"}],"emerging_topics":["mixture-of-agents","MoA"],"declining_topics":[],"summary":"This week sees the emergence of 'mixture-of-agents' as a new topic in AI discourse, with multiple high-importance articles covering this architectural approach. With no prior mentions in the past two weeks, this represents a genuinely new development worth watching."}
Example 3: Insufficient Data
Input:
{"current_period_articles":[{"title":"Random AI News","entities":["AI"],"importance_score":5,"published_at":"2026-01-26"}],"lookback_days":14,"min_mentions":3}
Output:
{"trends":[],"emerging_topics":[],"declining_topics":[],"summary":"Insufficient data for meaningful trend analysis. The current period contains too few articles or entities to identify significant trends. Consider increasing the analysis window or lowering the minimum mention threshold."}
Related Skills
analyze-trends - Basic trend detection without historical comparison
