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Journal Matchmaker

Name: Journal Matchmaker
Author: aipoch-ai

作者 AIpoch · GitHub ↗ · v1.0.0

cross-platform ⚠ suspicious

434

总下载

当前安装

版本数

在 OpenClaw 中安装

/install journal-matchmaker

功能描述

Recommend suitable high-impact factor or domain-specific journals for manuscript submission based on abstract content. Trigger when user provides paper abstr...

使用说明 (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 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

安全使用建议

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.

功能分析

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.

能力评估

✓ 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.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install journal-matchmaker
安装完成后，直接呼叫该 Skill 的名称或使用 /journal-matchmaker 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

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.

元数据

Slug journal-matchmaker

版本 1.0.0

许可证 —

累计安装 0

当前安装数 0

历史版本数 1

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