Journal 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.
SKILL.md
When to Use
- You have a manuscript title/abstract and need a shortlist of suitable journals to submit to.
- You want evidence-based journal matching using similar PubMed literature and journal distribution statistics.
- You need to compare candidate journals by scope fit, open access requirements, and review/publication timelines.
- You must provide a clear matching rationale (why each journal fits) for internal review or co-author alignment.
- You are planning a submission strategy (primary target + backups) and want to highlight risks and alternatives.
Key Features
- Topic- and abstract-driven journal recommendation workflow.
- PubMed-based similar literature search and journal frequency distribution compilation.
- Candidate journal screening using scope, policy constraints (e.g., OA), and practical considerations (e.g., review cycle).
- Structured recommendation output with rationale, risks, and backup options.
- Reusable CSV template for consistent reporting.
Dependencies
- Python 3.9+ (recommended)
- PubMed E-utilities access (NCBI)
EMAILrequired (per NCBI policy)API_KEYoptional (recommended for higher rate limits)
Example Usage
1) Prepare inputs
Have the manuscript title and abstract ready.
2) Configure the script
Open scripts/pubmed_journal_recommender.py and set the CONFIG values:
EMAIL: your email (required)API_KEY: your NCBI API key (optional)- Output directory (if the script supports/requests it)
3) Run the recommender
python scripts/pubmed_journal_recommender.py
When prompted, paste the manuscript title and abstract. The script will query PubMed for similar records and produce journal statistics.
4) Produce a structured recommendation table
Use the template below to standardize the final output:
- Template:
assets/journal_recommendation_template.csv
Fill it with:
- Candidate journals (from the script’s distribution + domain knowledge)
- Matching rationale (scope fit + audience + similarity evidence)
- Constraints (OA, policies)
- Practical notes (review cycle, risks)
- Primary target and backup options
5) Follow the checklist and formatting guidance
For recommended output formats, checklists, and key points, see:
references/guide.md
Implementation Details
Workflow Overview
-
Topic and Scope Definition
- Identify the research field, subfield, and intended readership.
- Confirm journal type preferences and constraints (e.g., OA mandates).
-
Similar Literature Analysis (PubMed)
- Use the manuscript title/abstract to retrieve similar PubMed records.
- Aggregate results by journal to compute a distribution (e.g., counts per journal).
- Prioritize journals that appear frequently among highly relevant records.
-
Journal Screening
- Cross-check each candidate against:
- Journal scope/aims
- Policy requirements (OA, data availability, ethics)
- Review/publication timelines (if available)
- Remove journals that are out-of-scope or non-compliant.
- Cross-check each candidate against:
-
Recommendation Output
- Provide a ranked list with:
- Fit rationale (topic alignment + similarity evidence)
- Risks (scope mismatch, policy conflicts, timeline concerns)
- Alternatives (backup journals)
- Provide a ranked list with:
Key Parameters / Notes
- NCBI
EMAIL: required to comply with NCBI E-utilities usage policy. - NCBI
API_KEY: optional but recommended to reduce throttling and improve throughput. - Output structuring: use
assets/journal_recommendation_template.csvto ensure consistent fields and downstream usability.