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Install in OpenClaw
/install journal-matchmaker
Description
Recommend suitable high-impact factor or domain-specific journals for manuscript submission based on abstract content. Trigger when user provides paper abstr...
README (SKILL.md)
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 scopesreferences/fields.json- Research field classificationsreferences/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
Usage Guidance
This skill appears coherent and limited to local processing of abstracts using the provided journal database. Before installing or running it: (1) Review the bundled references/journals.json if you rely on accurate impact factors (they can be stale); (2) Avoid passing sensitive or unpublished full manuscripts to any third-party runtime; run the script in an isolated/sandboxed workspace if you want extra safety; (3) If you allow passing filenames as --abstract, ensure the script treats them safely (SKILL.md mentions input validation — confirm the implementation prevents ../ path traversal when using file inputs); (4) Treat its recommendations as advisory (not authoritative) and double-check journal scope/IF via official sources before submission.
Capability Analysis
Type: OpenClaw Skill
Name: journal-matchmaker
Version: 1.0.0
The skill bundle is classified as suspicious due to a critical path traversal vulnerability in `scripts/main.py`. When the `--file` argument is used, the `args.abstract` parameter is directly used as a file path without any sanitization or validation, allowing an attacker to read arbitrary files on the system (e.g., `../../../../etc/passwd`). While the `SKILL.md`'s security checklist claims 'Input file paths validated (no ../ traversal)', the Python code does not implement this protection. There is no evidence of intentional malicious behavior like data exfiltration or persistence, but the vulnerability poses a significant security risk.
Capability Assessment
Purpose & Capability
Name and description match the included files: SKILL.md documents running scripts/main.py and the repository contains a local journal database and field definitions used for matching. There are no unexpected credentials, binaries, or third-party services required.
Instruction Scope
SKILL.md instructs the agent/user to run the bundled Python script with an abstract and optional filters. The instructions and the code (shown imports and local JSON references) operate on local files (references/*.json) and do keyword/TF-IDF matching; I saw no instructions to read unrelated system files, environment variables, or to send data to external endpoints.
Install Mechanism
No install spec is provided (instruction-only with a bundled script). Dependencies are minimal (requirements.txt contains only 'dataclasses'). Nothing is downloaded or extracted at install time, so there is no high-risk install mechanism.
Credentials
The skill declares no required environment variables, credentials, or config paths. The code imports only standard libraries and reads local JSON reference files; there are no requests for unrelated secrets or access to external accounts.
Persistence & Privilege
always is false (skill is not force-included). The skill does not request persistent system privileges or modify other skills' configuration. Its filesystem access is limited to reading/writing workspace files (per SKILL.md) and local references.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install journal-matchmaker - After installation, invoke the skill by name or use
/journal-matchmaker - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: recommends suitable journals for manuscript submission based on abstract content.
- Analyzes abstracts to identify research field and methodology.
- Matches papers to journals using scope alignment and impact factor filtering.
- Supports parameters for minimum impact factor, number of recommendations, and output format.
- Utilizes journal metadata and field classifications for ranking.
- Includes risk assessment, security checklist, and test cases for evaluation.
Metadata
Frequently Asked Questions
What is Journal Matchmaker?
Recommend suitable high-impact factor or domain-specific journals for manuscript submission based on abstract content. Trigger when user provides paper abstr... It is an AI Agent Skill for Claude Code / OpenClaw, with 434 downloads so far.
How do I install Journal Matchmaker?
Run "/install journal-matchmaker" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Journal Matchmaker free?
Yes, Journal Matchmaker is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Journal Matchmaker support?
Journal Matchmaker is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Journal Matchmaker?
It is built and maintained by AIpoch (@aipoch-ai); the current version is v1.0.0.
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