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".

53 stars

Best use case

target-journal-matcher is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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".

Teams using target-journal-matcher should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/target-journal-matcher/SKILL.md --create-dirs "https://raw.githubusercontent.com/aipoch/medical-research-skills/main/awesome-med-research-skills/Academic Writing/target-journal-matcher/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/target-journal-matcher/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How target-journal-matcher Compares

Feature / Agenttarget-journal-matcherStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this 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".

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

> **Source**: [https://github.com/aipoch/medical-research-skills](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 recommends suitable journals based on scope and fit analysis. Impact factor data is based on training knowledge and should be verified at the journal's official site before submission."

## 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)
**Tier 2 — Good fit** (solid IF, good scope match, realistic acceptance for this type of study)
**Tier 3 — Safe targets** (reliable acceptance for the design and evidence level, solid readership in the field)

For each journal, provide:
| Field | Content |
|---|---|
| **Journal name** | Full name |
| **Publisher** | |
| **Approx. IF** | Year range note (e.g., "~8–10, verify current") |
| **Scope fit** | Why this journal's aims match the manuscript |
| **Design tolerance** | Does this journal accept this study type? |
| **Strategic note** | Any 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?
3. **Impact realism** (0–2): Is the IF target realistic for a paper with this evidence strength?
4. **Practical fit** (0–2): OA requirements, speed, regional acceptability

Total ≥ 7/10 = Strong recommendation; 5–6 = Acceptable; <5 = Flag but include if user requested

### Step 4 — Deliver the Recommendation

Provide:
1. The tiered journal table with fit analysis
2. A **primary recommendation** (top single suggestion) with a 2–3 sentence justification
3. A **rejection strategy note**: if rejected from Tier 1, which Tier 2 should be next and why
4. An explicit note that IF data should be verified at the journal's official website before submission

## Key Domains and Representative Journals

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

| Domain | High-tier examples | Mid-tier examples |
|---|---|---|
| General medicine | NEJM, Lancet, JAMA, BMJ | JAMA Network Open, eClinicalMedicine |
| Oncology | JCO, Cancer Cell, Nature Cancer | Oncologist, Cancer Medicine |
| Cardiology | Circulation, JACC, EHJ | Heart, IJCS |
| Infectious disease | Lancet ID, CID | ID&I, JID |
| Bioinformatics/genomics | Nature Methods, Genome Biology | Briefings in Bioinformatics |
| Systematic review/meta-analysis | BMJ, Lancet, JAMA | Systematic Reviews, BMC SR |
| Prediction models | Lancet Digital Health | JAMIA, 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:
- Clarivate Journal Citation Reports (JCR): https://jcr.clarivate.com
- The journal's official "About" page

Related Skills

postdoc-fellowship-matcher

53
from aipoch/medical-research-skills

Filter and match postdoctoral fellowship opportunities based on applicant nationality, years since PhD, and research field from a curated database.

journal-recommender

53
from aipoch/medical-research-skills

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.

target-novelty-scorer

53
from aipoch/medical-research-skills

Score the novelty of biological targets through literature mining and.

open-targets-db

53
from aipoch/medical-research-skills

Query the Open Targets Platform to retrieve targets, diseases, or evidence records when you need target-disease association data and evidence-based scores for therapeutic discovery.

journal-skills

53
from aipoch/medical-research-skills

Recommends target journals for manuscript submission by analyzing the paper topic/abstract and the journal distribution of similar PubMed literature; use when users ask for journal recommendation/matching, submission strategy, PubMed search, or similar-literature statistics.

journal-matchmaker

53
from aipoch/medical-research-skills

Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.

journal-latest-issue

53
from aipoch/medical-research-skills

Retrieve the latest journal issue's table of contents and abstracts from URL/DOI/PMID/RSS/TOC sources, then generate Chinese key points locally (no external translation APIs) when a new issue needs quick review and archiving.

journal-impact-factor-trend

53
from aipoch/medical-research-skills

Show journal impact factor and quartile trends over 5 years.

journal-cover-prompter

53
from aipoch/medical-research-skills

Use when creating journal cover images, generating scientific artwork prompts, or designing graphical abstracts. Creates detailed prompts for AI image generators to produce publication-quality scientific visuals.

journal-club-presenter

53
from aipoch/medical-research-skills

Generate journal club slides with background, critique, and discussion.

medical-research-algorithm-matcher

53
from aipoch/medical-research-skills

Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.

drug-target-evidence-landscape

53
from aipoch/medical-research-skills

Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.