Emerging Topic Scout

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

Emerging Topic Scout

A real-time monitoring system for identifying "incubation period" research hotspots in biological and medical sciences before they are defined by mainstream journals.

When to Use

• Use this skill when the task needs A real-time monitoring system for identifying "incubation period" research hotspots in biological and medical sciences before they are defined by mainstream journals.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: A real-time monitoring system for identifying "incubation period" research hotspots in biological and medical sciences before they are defined by mainstream journals.
• Packaged executable path(s): scripts/main.py plus 1 additional script(s).
• 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.
• dataclasses: unspecified. Declared in requirements.txt.
• feedparser: unspecified. Declared in requirements.txt.
• requests: unspecified. Declared in requirements.txt.
• textblob: unspecified. Declared in requirements.txt.
• requests: >=2.28.0. Declared in scripts/requirements.txt.
• feedparser: >=6.0.10. Declared in scripts/requirements.txt.
• pandas: >=1.5.0. Declared in scripts/requirements.txt.
• scikit-learn: >=1.1.0. Declared in scripts/requirements.txt.
• numpy: >=1.23.0. Declared in scripts/requirements.txt.
• textblob: >=0.17.1. Declared in scripts/requirements.txt.
• pyyaml: >=6.0. Declared in scripts/requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/emerging-topic-scout"
python -m py_compile scripts/main.py
python scripts/main.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/main.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/main.py with additional helper scripts under scripts/.
• 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.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/smoke_test.py

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Audit Note

The primary script depends on optional external packages such as textblob and live-source access. Audit validation therefore uses scripts/smoke_test.py as the deterministic fallback command for structural verification in constrained environments.

Overview

This skill continuously monitors:

• bioRxiv: Biology preprints via RSS/API ⚠️ Currently blocked by Cloudflare
• medRxiv: Medicine preprints via RSS/API ⚠️ Currently blocked by Cloudflare
• arXiv: Quantitative Biology preprints via RSS ✅ Recommended alternative
• Academic discussions: Social media and forum mentions

It uses trend analysis algorithms to detect sudden spikes in topic frequency, cross-platform mentions, and emerging keyword clusters.

⚠️ Network Access Notice

bioRxiv and medRxiv are currently protected by Cloudflare JavaScript Challenge, which prevents programmatic RSS access. As a workaround, this skill now supports arXiv q-bio (Quantitative Biology) as an alternative data source.

Recommended usage:

# Use arXiv for reliable data fetching
python scripts/main.py --sources arxiv --days 30

# bioRxiv/medRxiv may return 0 results due to Cloudflare protection
python scripts/main.py --sources biorxiv medrxiv --days 30  # May not work

Installation

cd /Users/z04030865/.openclaw/workspace/skills/emerging-topic-scout
pip install -r scripts/requirements.txt

Usage

Basic Scan (Recommended: Use arXiv)

python scripts/main.py --sources arxiv --days 7 --output json

Legacy bioRxiv/medRxiv (May not work due to Cloudflare)

python scripts/main.py --sources biorxiv medrxiv --days 7 --output json

Advanced Configuration (arXiv Recommended)

python scripts/main.py \
  --sources arxiv \
  --keywords "CRISPR,gene editing,machine learning" \
  --days 14 \
  --min-score 0.7 \
  --output markdown \
  --notify