journal-matchmaker

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.

1.28x

Quality

86%

Does it follow best practices?

Impact

95%

1.28x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Journal Matchmaker

Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.

Use Cases

Find the best-fit journal for a new manuscript
Identify high-impact factor journals in specific research areas
Compare journal scopes against paper content
Discover domain-specific publication venues

Usage

python scripts/main.py --abstract "Your paper abstract text here" [--field "field_name"] [--min-if 5.0] [--count 5]

Parameters

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

Examples

# 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

How It Works

Abstract Analysis: Extracts key terms, methodology, and research focus
Field Classification: Identifies the primary research domain
Journal Matching: Compares content against journal scopes and aims
Impact Factor Filtering: Applies IF constraints if specified
Ranking: Scores and ranks journals by relevance and impact

Technical Details

Difficulty: Medium
Approach: Keyword extraction + journal database matching
Data Source: Journal metadata from references/journals.json
Algorithm: TF-IDF + cosine similarity for scope matching

References

references/journals.json - Journal database with impact factors and scopes
references/fields.json - Research field classifications
references/scoring_weights.json - Algorithm tuning parameters

Notes

Journal database should be updated periodically (quarterly recommended)
Impact factor data sourced from Journal Citation Reports (JCR)
Scope descriptions parsed from official journal websites
For emerging fields, manual curation may be needed

Risk Assessment

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

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access (../)
Output does not expose sensitive information
Prompt injection protections in place
Input file paths validated (no ../ traversal)
Output directory restricted to workspace
Script execution in sandboxed environment
Error messages sanitized (no stack traces exposed)
Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

Successfully executes main functionality
Output meets quality standards
Handles edge cases gracefully
Performance is acceptable

Test Cases

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
Performance: Large dataset → Acceptable processing time

Lifecycle Status

Current Stage: Draft
Next Review Date: 2026-03-06
Known Issues: None
Planned Improvements:
- Performance optimization
- Additional feature support

Repository: aipoch/medical-research-skills
Commit: 4a48721

Last updated: about 12 hours ago
Created: about 12 hours ago

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