research-hotspot-analysis

Source: https://github.com/aipoch/medical-research-skills

Output Format

Output must strictly follow this structured report format to ensure users receive directly readable decision-ready content rather than a simple text description.

1. Hotspot Overview Table

Display all identified hotspot topics and their popularity metrics in tabular form.

Hotspot Topic	Popularity Index	Trend Direction	Representative Papers	Active Years
[Topic 1]	★★★★★	Rising	45 papers	2023-2026
[Topic 2]	★★★★	Stable	28 papers	2022-2026
[Topic 3]	★★★	Declining	12 papers	2020-2024

Popularity Index: Composite score based on publication volume, citation frequency, and top-tier journal proportion (max ★★★★★).
Trend Direction: Recent 3-year publication trend (Rising / Stable / Declining).
Representative Papers: Number of core papers matching the hotspot topic.
Active Years: Year range with sustained output for the hotspot.

2. Hotspot Detail Analysis

Each hotspot is expanded independently, including popularity rating, trend description, sub-direction composition, and key papers.

Hotspot 1: [Topic Name]

Popularity Index: ★★★★★ (Very Hot)
Trend: Rising publication volume over the past 3 years
Core Research Directions:
- Sub-direction A (40%)
- Sub-direction B (35%)
Key Papers:

Paper	Journal	Year	Citations	Evidence Level
[Title]	Nature	2025	230	High
[Title]	Cell Rep	2024	98	Medium
[Title]	Front Immunol	2024	15	Low

Evidence Level: High (top-tier/highly cited), Medium (mainstream/moderate citations), Low (lower-tier/few citations).

Hotspot 2: [Topic Name]

...

3. Research Gap Identification

Analyze under-explored areas in current literature.

Research Gap	Potential Value	Feasibility	Reason
[Gap 1 - underexplored direction]	High	Medium	Few than 10 papers, but clear clinical demand
[Gap 2]	Medium	High	Mature tools available, but not yet applied in this field

Potential Value: High / Medium / Low, based on unmet clinical or basic research needs.
Feasibility: High / Medium / Low, based on technical maturity, research barriers, and execution difficulty.

4. Recommended Entry Directions

Based on the preceding analysis, provide concrete actionable research entry suggestions.

Priority	Recommended Direction	Reason	Expected Output
1	[Direction A]	High popularity + existing gap + good feasibility	1 review / 1 experimental design
2	[Direction B]	Emerging hotspot + low competition	1-2 research papers
3	[Direction C]	Niche but high clinical value	Case series / methodology paper

Research Hotspot Analysis

When to Use

The user provides a disease name, target, technical roadmap, or research topic, and wants to quickly see current research hotspots.
The user needs to cluster recent literature by keywords and topics to find directions worth deeper exploration.
The user wants a Markdown hotspot analysis report with representative literature for topic selection or review writing.

When Not to Use

Do not use this skill when the user only needs single-paper retrieval or a simple reference list.
If there is no clear disease, topic, or search scope, do not start clustering immediately — first ask the user to clarify topic boundaries.
If the environment cannot access the scripts or retrieval data this skill depends on, do not fabricate hotspot results.

Required Inputs

Field	Required	Format/Source	Example	If Missing
`topic`	Yes	Text	`lung cancer immunotherapy`	Stop and request topic
`time_range`	No	Time range	`last 5 years`	Default to recent literature
`focus`	No	Text	`mechanism`, `clinical translation`	Default to comprehensive hotspot output

Workflow

Use search_pubmed from scripts/analysis_ops.py to search relevant literature and obtain PMIDs and basic metadata.
Run word_frequency on the returned medline_texts to count high-frequency keywords or MeSH terms.
Combine with hotspot prompts in references/prompt_templates.md to cluster high-frequency keywords into 3-6 hotspot topics.
Use match_keywords to map representative literature to each topic, avoiding mismatches between topics and evidence.
For each topic, call sort_by_jif_and_select to choose representative literature, then use fetchPMCArticleDetails or fetchPubmedArticleDetails to supplement details.
Output a Markdown report with at least: research overview, hotspot topics, representative keywords per topic, representative literature, and follow-up suggestions.

Output Contract

Primary output: A Markdown hotspot analysis report.
Required fields: topic overview, hotspot topics, supporting papers, next-step suggestions.
Recommend at least 2-3 representative papers per hotspot, with explanation of why the topic qualifies as a hotspot.
If retrieval coverage is insufficient, must explicitly mark as PARTIAL.

Failure Handling

Too few literature search results: First broaden time range or relax keywords, then explain coverage gaps.
Unstable keyword clustering: Show high-frequency keywords and indicate clustering is candidate-only — do not force conclusions.
Representative literature lacks usable details: Keep PMID and title, mark as pending.

User Checkpoints

Before starting a broad search, confirm topic boundaries and time range.
Before outputting the final hotspot report, if cluster topics are clearly ambiguous, send candidate topics to user for confirmation.

Tools

fetchPMCArticleDetails: Get article details.
fetchPubmedArticleDetails: Get PubMed details.

Scripts

scripts/analysis_ops.py: Contains helper functions for PubMed search, frequency analysis, keyword matching, and result formatting.

References

references/prompt_templates.md: Contains the system prompts for LLM analysis.

Input Validation

This skill accepts requests that match the documented purpose of research-hotspot-analysis and include enough context to complete the workflow safely.

Quick Validation

Check that scripts/analysis_ops.py exists and can perform at least the three core steps: search, word frequency, and matching.
Check that the final report contains hotspot topics with corresponding representative literature, not just a keyword list.
Check that each hotspot topic has clear evidence sources to support it.