Agent skill

Journal Metrics Guide

by wentorai in wentorai/research-plugins

Understand journal impact factors, h5-index, CiteScore, and SJR

MITAuto-check passedResearch & Science

Install Journal Metrics Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill journal-metrics-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins journal-metrics-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/literature/metadata/journal-metrics-guide .claude/skills/journal-metrics-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
journal-metrics-guide
GitHub stars
298
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
203 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Understand journal impact factors, h5-index, CiteScore, and SJR

  • Research & Science work in your project
  • SKILL.md covers Overview of Major Metrics, How Key Metrics Are Calculated, Finding Journal Metrics and Responsible Use of Metrics, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Journal Metrics Guide is an agent skill from wentorai/research-plugins. Understand journal impact factors, h5-index, CiteScore, and SJR

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/journal-metrics-guide”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Journal Metrics Guide loads about 1.6k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 203 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 203 words, ~1,603 tokens.

Download SKILL.mdSave it as .claude/skills/journal-metrics-guide/SKILL.md (or your agent's skills folder).
name
journal-metrics-guide
description
Understand journal impact factors, h5-index, CiteScore, and SJR

Journal Metrics Guide

A skill for understanding, comparing, and responsibly using journal-level metrics including Impact Factor, CiteScore, SJR, SNIP, h5-index, and Eigenfactor. Covers where to find each metric, how they are calculated, their limitations, and how to select appropriate journals for submission.

Overview of Major Metrics

Metric Comparison
MetricProviderFormula BasisWindowFree?
Impact Factor (JIF)Clarivate (JCR)Citations to articles / citable items2 yearsNo
5-Year IFClarivate (JCR)Same, extended window5 yearsNo
CiteScoreScopus/ElsevierCitations / documents4 yearsYes
SJRScopus/SCImagoPrestige-weighted citations3 yearsYes
SNIPScopus/CWTSCitation potential normalized3 yearsYes
h5-indexGoogle Scholarh-index of articles published in last 5 years5 yearsYes
EigenfactorClarivateNetwork citation influence5 yearsYes

How Key Metrics Are Calculated

Journal Impact Factor
JIF (2024) = Citations in 2024 to articles published in 2022-2023
             -------------------------------------------------------
             Number of citable items published in 2022-2023

Example:
  Journal published 200 articles in 2022-2023
  Those articles received 1,000 citations in 2024
  JIF = 1000 / 200 = 5.0
CiteScore
CiteScore (2024) = Citations in 2021-2024 to items published in 2021-2024
                   -----------------------------------------------------------
                   Documents published in 2021-2024

Key difference from JIF:
  - 4-year window (vs. 2 years)
  - Includes ALL document types in denominator (not just "citable items")
  - More transparent calculation
  - Freely available at scopus.com/sources
SJR (SCImago Journal Rank)
python
def explain_sjr() -> dict:
    """
    Explain the SCImago Journal Rank metric.
    """
    return {
        "concept": (
            "SJR weights citations by the prestige of the citing journal. "
            "A citation from Nature counts more than a citation from a "
            "low-prestige journal."
        ),
        "algorithm": "Based on Google PageRank applied to citation network",
        "range": "Typically 0.1 to 20+; most journals 0.2-2.0",
        "lookup": "scimagojr.com - free access",
        "quartiles": {
            "Q1": "Top 25% of journals in the subject category",
            "Q2": "25th-50th percentile",
            "Q3": "50th-75th percentile",
            "Q4": "Bottom 25%"
        }
    }

Finding Journal Metrics

Free Sources
Google Scholar Metrics:
  scholar.google.com/citations?view_op=top_venues
  - h5-index and h5-median for thousands of venues
  - Filtered by broad discipline or specific subcategory
  - Updated annually

SCImago Journal Rank:
  scimagojr.com
  - SJR, h-index, total documents, total citations
  - Country and subject area filtering
  - Free journal comparison tool

Scopus Sources:
  scopus.com/sources
  - CiteScore, SJR, SNIP for all Scopus-indexed journals
  - CiteScore Tracker (real-time estimate)
  - Free with Scopus account
Subscription Sources
Journal Citation Reports (JCR):
  Clarivate Analytics (institutional subscription)
  - Impact Factor, 5-Year IF, Eigenfactor
  - Journal quartile rankings by category
  - Cited/citing journal networks

InCites:
  Clarivate Analytics
  - Normalized citation impact at journal and article level
  - Benchmarking tools

Responsible Use of Metrics

Limitations and Pitfalls
1. Field dependence:
   - Life sciences JIF >> Computer science JIF
   - Never compare JIF across disciplines

2. Skewed distributions:
   - A few highly cited papers inflate the average
   - Median citations per article is more representative

3. Gaming and manipulation:
   - Excessive self-citation
   - Citation cartels between journals
   - Review articles inflating citation counts

4. Not a measure of individual paper quality:
   - A paper in a high-IF journal may receive zero citations
   - A paper in a modest journal may become highly influential

5. DORA declaration:
   - Over 2,500 organizations signed the San Francisco
     Declaration on Research Assessment (DORA)
   - Recommends against using JIF as a proxy for
     individual article quality in hiring, promotion,
     or funding decisions
Choosing a Journal for Submission
python
def evaluate_journal_fit(metrics: dict, paper_profile: dict) -> dict:
    """
    Evaluate journal suitability beyond just impact factor.

    Args:
        metrics: Journal metrics (JIF, CiteScore, acceptance rate, etc.)
        paper_profile: Characteristics of your manuscript
    """
    criteria = {
        "scope_match": "Does the journal publish papers on this topic?",
        "audience": "Will the right readers see this paper here?",
        "turnaround": "What is the average time from submission to decision?",
        "open_access": "Does the journal offer OA options? What are the APCs?",
        "acceptance_rate": "Is the acceptance rate realistic for this paper?",
        "indexing": "Is the journal indexed in Scopus, WoS, PubMed?",
        "prestige": "How is this journal perceived in your specific subfield?",
        "ethics": "Is the journal a member of COPE? Does it follow best practices?"
    }

    return {
        "recommendation": "Consider all factors, not just metrics",
        "criteria": criteria,
        "warning": (
            "Avoid predatory journals. Check Beall's list and "
            "verify the journal is indexed in recognized databases."
        )
    }

Predatory Journal Detection

Watch for these warning signs: unsolicited email invitations to submit, very rapid peer review (days), lack of indexing in Scopus or Web of Science, vague editorial board, no clear ISSN, and APCs that seem unusually low. Use resources like "Think. Check. Submit." (thinkchecksubmit.org) to verify journal legitimacy.

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/literature/metadata/journal-metrics-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

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Questions about Journal Metrics Guide

What does Journal Metrics Guide do?

Understand journal impact factors, h5-index, CiteScore, and SJR. Journal Metrics Guide is an agent skill from wentorai/research-plugins.

When should I use Journal Metrics Guide?

Journal Metrics Guide fits situations like: research & Science work in your project.

How do I install Journal Metrics Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill journal-metrics-guide -a claude-code`. Or copy the skill folder (skills/literature/metadata/journal-metrics-guide in wentorai/research-plugins) into .claude/skills/journal-metrics-guide in your project. Claude Code loads it when a task matches its description.

How do I install Journal Metrics Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill journal-metrics-guide -a codex`. Or copy the skill folder (skills/literature/metadata/journal-metrics-guide in wentorai/research-plugins) into .agents/skills/journal-metrics-guide in your project. Codex loads it when a task matches its description.

Can I use Journal Metrics Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill journal-metrics-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/journal-metrics-guide, .gemini/skills/journal-metrics-guide, .github/skills/journal-metrics-guide and .opencode/skills/journal-metrics-guide in your project.

What does Journal Metrics Guide need to run?

SKILL.md names no scripts, command-line tools or credentials: Journal Metrics Guide is instructions for the agent only. Our summary lists: Python 3.

Does Journal Metrics Guide access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Journal Metrics Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Journal Metrics Guide use?

Journal Metrics Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Journal Metrics Guide use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Journal Metrics Guide?

Skills that share tags, products or a category with Journal Metrics Guide: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Journal Metrics Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.