trend-analysis

name	trend-analysis
description	This skill should be used when the user asks to "analyze trends in a research area", "what is trending in X", "research landscape for topic Y", "topic trend analysis", "emerging themes in machine learning", "what are researchers working on in Z", or wants to survey a field and identify emerging patterns across multiple papers using PaperBot MCP tools.
tools	["paper_search","analyze_trends","get_research_context","save_to_memory"]

Trend Analysis Workflow

Identify research trends across a topic by collecting papers, analyzing patterns, and saving a synthesis of emerging themes.

Workflow

Step 1: Load research context (optional)

If a research track exists for the topic, call get_research_context to retrieve existing memories and previously found papers.

• Parameters: query (the research topic), user_id (default "default"), track_id (optional; pass if a specific track ID is known)
• Returns: dict with papers (list), memories (list), stage (workflow stage string)
• Use the existing memories as context when synthesizing results in Step 4
• Skip this step if no prior research context exists for the topic

Step 2: Search for papers

Call paper_search with the topic. Use a broader corpus for trend analysis.

• Parameters: query (required), max_results (use 20–50 for trend analysis — a larger corpus improves trend signal quality), sources (optional)
• Returns: list of paper dicts with title, abstract, authors, year, venue
• If track_id context was loaded in Step 1, merge the existing papers with new results (deduplicate by arxiv_id or doi)

Step 3: Analyze trends

Call analyze_trends with the topic and the list of papers from Step 2.

• Parameters: topic (the research area string), papers (list of paper dicts from paper_search; pass the full list for best results)
• Returns: dict with trend_analysis (natural language narrative), topic, paper_count
• Check for degraded=True — analyze_trends requires a configured LLM API key

Step 4: Save synthesis

Call save_to_memory with the trend analysis narrative and any additional observations.

• Parameters: content (the trend_analysis text from Step 3, optionally enhanced with your own observations), kind ("note" for factual observations, "hypothesis" for directional predictions), user_id (default "default"), scope_type ("global" for broad field trends, "track" if scoping to a research area), scope_id (track ID if scope_type="track"), confidence (0.0–1.0)
• Returns: dict with created or skipped status

Degraded Mode

analyze_trends requires a configured LLM API key. paper_search and get_research_context work without LLM.

When analyze_trends returns degraded=True:

• The response also contains an error key describing the issue
• Set OPENAI_API_KEY or ANTHROPIC_API_KEY and restart the MCP server
• In degraded mode, present the raw search results grouped by year or venue as a manual trend signal; skip Step 3 or surface the paper list to the user directly

Notes

• For fast trend snapshots, use max_results=20 and skip Step 1
• For deep research landscape maps, use max_results=50 and integrate prior context from get_research_context
• When analyzing sub-field trends (e.g., "sparse attention mechanisms"), narrow the query rather than broadening max_results
• Multiple calls with different topic variants (e.g., "mixture of experts" vs. "sparse expert models") can be combined for a richer landscape view