Skill: aipoch-ai/journal-matchmaker (version 1.0.0)

--- name: journal-matchmaker description: 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. version: 1.0.0 category: Research tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # 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 ```bash 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 ```bash # 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 1. **Abstract Analysis**: Extracts key terms, methodology, and research focus 2. **Field Classification**: Identifies the primary research domain 3. **Journal Matching**: Compares content against journal scopes and aims 4. **Impact Factor Filtering**: Applies IF constraints if specified 5. **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 ```bash # 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 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **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

path: requirements.txt
content: dataclasses


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)