Recommend suitable high-impact factor or domain-specific journals for manuscript submission based on abstract content. Trigger when user provides paper abstract and asks for journal recommendations, impact factor matching, or scope alignment suggestions.
Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.
python scripts/main.py --abstract "Your paper abstract text here" [--field "field_name"] [--min-if 5.0] [--count 5]
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
--abstract | str | Yes | - | Paper abstract text to analyze |
--field | str | No | Auto-detect | Research field (e.g., "computer_science", "biology") |
--min-if | float | No | 0.0 | Minimum impact factor threshold |
--max-if | float | No | None | Maximum impact factor (optional) |
--count | int | No | 5 | Number of recommendations to return |
--format | str | No | table | Output format: table, json, markdown |
# Basic usage
python scripts/main.py --abstract "This paper presents a novel deep learning approach..."
# Specify field and minimum impact factor
python scripts/main.py --abstract "abstract.txt" --field "ai" --min-if 10.0 --count 10
# Output as JSON for integration
python scripts/main.py --abstract "..." --format json
references/journals.json - Journal database with impact factors and scopesreferences/fields.json - Research field classificationsreferences/scoring_weights.json - Algorithm tuning parameters| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
# Python dependencies
pip install -r requirements.txt