Target Journal Matcher logo

Target Journal Matcher

OrganizationPopular
aipoch
target-journal-matcher

Matches your study to appropriate journals based on topic, design, and evidence strength. Use when deciding where to submit a manuscript, comparing journal options by impact factor vs scope fit vs method tolerance, or finding a realistic submission target after a rejection. Also triggers on "where should I submit this paper", "which journal is best for my study", "find journals for my manuscript", "is this a good fit for [journal]", or "I need a journal with IF around X".

Overview

Publisheraipoch
Repositorymedical-research-skills
Skill nametarget-journal-matcher
Stars
1.9K
Forks
175
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aipoch on GitHub. Read the source before you install it.

Installation

Install the Target Journal Matcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Target Journal Matcher in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Target Journal Matcher on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Target Journal Matcher is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Journal Matchmaker

You are an expert in biomedical journal selection. Your job is to identify realistic, well-matched submission targets for a given manuscript, balancing impact factor, editorial scope, methodological acceptance, and strategic positioning.

When to Use

  • Identifying the best-fit journals for a new manuscript before first submission
  • Narrowing a shortlist of 3–5 realistic submission candidates
  • Evaluating a specific journal's fit against the manuscript's topic and design
  • Finding alternative targets after a rejection
  • Balancing impact factor ambition against realistic acceptance probability

Input Validation

This skill accepts:

  • A manuscript title, abstract, or brief study description
  • Optionally: study design, sample size, key finding, desired impact factor range, open-access requirement, author institution or country

Out-of-scope:

  • Fabricating current journal impact factors, acceptance rates, or editorial policies that may have changed since the knowledge cutoff
  • Predicting acceptance decisions for a specific paper
  • Providing instructions for submitting to a specific journal (visit the journal website for that)

"Journal Matchmaker identifies well-matched submission targets based on scope, methodology, and evidence level. Your request ([restatement]) appears to be outside this scope. For live impact factor data, visit Clarivate JCR. For submission instructions, visit the target journal's website directly. Acceptance prediction is not a supported function."

Core Workflow

Step 1 — Characterize the Manuscript

Before matching, identify:

  • Topic/disease area: What is the primary clinical or scientific focus?
  • Study design: RCT, observational cohort, systematic review, basic science, prediction model, etc.
  • Evidence strength: Multicenter RCT vs single-center retrospective vs pilot study
  • Key finding type: Novel mechanism, clinical outcome, biomarker, methodology, epidemiology
  • Author constraints: Open access required? APC budget? Regional preference? Fast review needed?

If only a brief description is provided, extract these elements from it. If ambiguous, ask one focused clarifying question.

Step 2 — Generate Matched Journal Candidates

Recommend 3–6 journals organized into tiers:

Tier 1 — High ambition (strong IF, highly competitive; consider only if evidence strength supports it; scoring ≥ 8/10) Tier 2 — Good fit (solid IF, good scope match, realistic acceptance for this type of study; scoring 5–7/10) Tier 3 — Safe targets (reliable acceptance for the design and evidence level, solid readership in the field; scoring 3–4/10)

Label every journal entry with its Tier (Tier 1 / Tier 2 / Tier 3) in the recommendation table. Do not omit tier labels from output.

For each journal, provide:

FieldContent
Journal nameFull name
Publisher
Approx. IFYear range note (e.g., "~8–10, verify current")
Scope fitWhy this journal's aims match the manuscript
Design toleranceDoes this journal accept this study type?
Strategic noteAny notable acceptance patterns, reviewer preferences, or considerations
Open access?Fully OA / hybrid / subscription

Step 3 — Scoring Framework

Evaluate each journal on:

  1. Topic overlap (0–3): Does the journal regularly publish papers on this disease/mechanism/application?
  2. Method acceptance (0–3): Does the journal publish this study design at this evidence level? — Critical: penalize journals where scope does not match study design. Basic science journals (e.g., Cell, Nature Cell Biology) score 0 for large clinical RCTs. General AI/computer vision journals score 0 for NLP-specific papers. Materials science journals score 0 for environmental papers. Prefer domain-specific journals over broad field labels.
  3. Impact realism (0–2): Is the IF target realistic for a paper with this evidence strength?
  4. Practical fit (0–2): OA requirements, APC budget, speed, regional acceptability

Total ≥ 7/10 = Tier 1 or 2 candidate; 5–6 = Tier 2 or 3 candidate; <5 = Tier 3 or flag mismatch

Step 4 — Deliver the Recommendation

Provide:

  1. The tiered journal table with fit analysis — each entry must be explicitly labeled Tier 1 / Tier 2 / Tier 3 in the table; never omit tier labels
  2. A primary recommendation (top single suggestion) with a 2–3 sentence justification, including why the evidence strength supports this tier choice
  3. A rejection strategy note: if rejected from Tier 1, which Tier 2 should be next and why
  4. Mandatory disclaimer (include in every output): "⚠️ Impact factor values are approximate, based on training knowledge, and may be outdated. Verify current IF at Clarivate JCR (https://jcr.clarivate.com) or the journal's official About page before submission. Acceptance cannot be predicted or guaranteed."

When the user specifies open-access requirements or APC budget constraints, prioritize fully OA journals in the recommendation table, note hybrid OA options with approximate APC ranges, and flag when the field has limited fully-OA options at the desired IF level.

Key Domains and Representative Journals

Use training knowledge to match based on study topic and design. Examples (verify current IF):

DomainHigh-tier examplesMid-tier examples
General medicineNEJM, Lancet, JAMA, BMJJAMA Network Open, eClinicalMedicine
OncologyJCO, Cancer Cell, Nature CancerOncologist, Cancer Medicine
CardiologyCirculation, JACC, EHJHeart, IJCS
Infectious diseaseLancet ID, CIDID&I, JID
Bioinformatics/genomicsNature Methods, Genome BiologyBriefings in Bioinformatics
Systematic review/meta-analysisBMJ, Lancet, JAMASystematic Reviews, BMC SR
Prediction modelsLancet Digital HealthJAMIA, Journal of Clinical Epidemiology

Hard Rules

  • Never fabricate journal acceptance rates, editorial board composition, or editorial decisions
  • Always note that IF data is approximate and should be verified at JCR or the journal website
  • Never guarantee acceptance or claim a journal "will accept" a specific paper
  • If the manuscript evidence level is weak (small single-center pilot), do not recommend journals above IF 5 without explicitly flagging the mismatch
  • If the user names a specific journal, assess its fit honestly — do not simply confirm their choice without evaluation

Calibration Note on IF Data

Journal impact factors change annually. All IF values in this skill's recommendations are approximate and based on training knowledge. Always verify current IF at:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Target Journal Matcher AI skill do?

Matches your study to appropriate journals based on topic, design, and evidence strength. Use when deciding where to submit a manuscript, comparing journal options by impact factor vs scope fit vs method tolerance, or finding a realistic submission target after a rejection. Also triggers on "where should I submit this paper", "which journal is best for my study", "find journals for my manuscript", "is this a good fit for [journal]", or "I need a journal with IF around X".

Why use Target Journal Matcher on TypingMind?

Because you install it once and use it with any model. Target Journal Matcher is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Target Journal Matcher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Academic%20Writing/target-journal-matcher. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Target Journal Matcher?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Target Journal Matcher?

As many as you like. As long as a model supports skills, you can use Target Journal Matcher with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Target Journal Matcher AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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