Agent Skills

Research Hotspot Analysis

AIPOCH

Analyzes research hotspots and recommends literature based on a disease or topic. Use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease.

3
0
FILES
research-hotspot-analysis/
skill.md
scripts
analysis_ops.py
references
prompt_templates.md
86100Total Score
View Evaluation Report
Core Capability
77 / 100
Functional Suitability
9 / 12
Reliability
9 / 12
Performance & Context
8 / 8
Agent Usability
12 / 16
Human Usability
7 / 8
Security
8 / 12
Maintainability
9 / 12
Agent-Specific
15 / 20
Medical Task
20 / 20 Passed
96Analyzes research hotspots and recommends literature based on a disease or topic
4/4
92Analyzes research hotspots and recommends literature based on a disease or topic
4/4
90Analyzes research hotspots and recommends literature based on a disease or topic
4/4
90Packaged executable path(s): scripts/analysis_ops.py
4/4
90End-to-end case for Scope-focused workflow aligned to: Analyzes research hotspots and recommends literature based on a disease or topic. Use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease
4/4

SKILL.md

Research Hotspot Analysis

When to Use

  • Use this skill when you need analyzes research hotspots and recommends literature based on a disease or topic. use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease in a reproducible workflow.
  • Use this skill when a evidence insight task needs a packaged method instead of ad-hoc freeform output.
  • Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
  • Use this skill when scripts/analysis_ops.py is the most direct path to complete the request.
  • Use this skill when you need the research-hotspot-analysis package behavior rather than a generic answer.

Key Features

  • Scope-focused workflow aligned to: Analyzes research hotspots and recommends literature based on a disease or topic. Use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease.
  • Packaged executable path(s): scripts/analysis_ops.py.
  • Reference material available in references/ for task-specific guidance.
  • Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

  • Python: 3.10+. Repository baseline for current packaged skills.
  • Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

See ## Usage above for related details.

cd "20260316/scientific-skills/Evidence Insight/research-hotspot-analysis"
python -m py_compile scripts/analysis_ops.py
python scripts/analysis_ops.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/analysis_ops.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

  • Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
  • Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
  • Primary implementation surface: scripts/analysis_ops.py.
  • Reference guidance: references/ contains supporting rules, prompts, or checklists.
  • Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
  • Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Description

This skill analyzes research hotspots for a given disease or topic by searching recent literature, calculating keyword frequencies, clustering topics, and recommending high-impact papers.

Usage

  1. Input: The user provides a disease name or research topic (e.g., "Lung Cancer", "Diabetes").
  2. Process:
    • Searches for recent literature (PMIDs) using the internal literature database.
    • Analyzes MESH terms to calculate word frequency and identify top keywords.
    • Uses LLM to cluster keywords into "Hotspot Topics".
    • Matches specific PMIDs to each topic.
    • Fetches full details (PMC) for top-ranked papers (by JIF/Availability).
    • Generates a comprehensive report with an introduction and detailed hotspot analysis.
  3. Output: A Markdown report containing the research overview and specific paper recommendations per hotspot.

Workflow

  1. Search Literature: Use scripts/analysis_ops.py (search_pubmed) to find relevant PMIDs and fetch details.
  2. Analyze Keywords: Use scripts/analysis_ops.py (word_frequency) on the medline_texts output from Step 1 to find top MESH terms.
  3. Identify Topics: Use LLM with references/prompt_templates.md (Hotspot Analysis) to group keywords into topics.
  4. Match Evidence: Use scripts/analysis_ops.py (match_keywords) with documents from Step 1 to map PMIDs to topics.
  5. Fetch Details: For each topic, select top papers using scripts/analysis_ops.py (sort_by_jif_and_select) and fetch details using fetchPMCArticleDetails.
  6. Generate Report: Synthesize the findings into a final report using LLM.

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.