name: journal-recommender description: Recommend academic journals based on manuscript topic, abstract, and impact factor expectations. Use when the user wants to find suitable journals for their research manuscript, especially when they provide a topic, abstract, and target Impact Factor. license: MIT author: aipoch

Source: https://github.com/aipoch/medical-research-skills

Journal Recommender

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Recommend academic journals based on manuscript topic, abstract, and impact factor expectations. Use when the user wants to find suitable journals for their research manuscript, especially when they provide a topic, abstract, and target Impact Factor.
• Packaged executable path(s): scripts/journal_ranker.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

See ## Usage above for related details.

cd "20260316/scientific-skills/Others/journal-recommender"
python -m py_compile scripts/journal_ranker.py
python scripts/journal_ranker.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/journal_ranker.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Overview above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/journal_ranker.py.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Overview

This skill analyzes a research manuscript (topic, abstract, and optional full text) to extract key information (keywords, field, workload, innovation) and recommends journals in three categories: Sprint (High), Robust (Match), and Safe (Low).

Workflow

1. Assess Manuscript:

   • Analyze the provided topic and abstract.
   • Extract keywords and determine the specific research field.
   • Evaluate the workload and innovation of the study.
   • Estimate the manuscript's potential Impact Factor (IF).

2. Recommend Journals:

   • Based on the assessment and the user's target_if, search for and recommend journals.
   • Categorize recommendations into:
     • Sprint Journals: IF slightly higher than target (max +5).
     • Robust Journals: IF matches the target and assessment.
     • Safe Journals: IF lower than target, ensuring high acceptance chance.
   • Ensure at least 5 journals per category.
   • Constraint: Do not recommend journals from the CAS warning list.

Usage

Inputs

• topic (Required): The title or topic of the manuscript.
• abstract (Required): The abstract of the manuscript.
• target_if (Required): The expected Impact Factor (number).
• manuscript (Optional): Full text of the manuscript.
• article_type (Default: "research article"): Type of the article.

Deterministic Operations

• Sorting: The recommended journals are sorted by Impact Factor in descending order using scripts/journal_ranker.py.

Quality Rules

• IF Sorting: Journals must be strictly sorted by IF.
• Safety: No CAS warning journals are allowed.
• Quantity: Minimum 5 journals per category.

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

• A clearly specified task goal aligned with the documented scope.
• All required files, identifiers, parameters, or environment variables before execution.
• Any domain constraints, formatting requirements, and expected output destination if applicable.

Output Contract

• Return a structured deliverable that is directly usable without reformatting.
• If a file is produced, prefer a deterministic output name such as journal_recommender_result.md unless the skill documentation defines a better convention.
• Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

• Validate required inputs before execution and stop early when mandatory fields or files are missing.
• Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
• Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
• Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

• If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
• If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
• If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

python scripts/journal_ranker.py --help

Expected output format:

Result file: journal_recommender_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any