| 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
