Agent Skills

Journal Skills

AIPOCH

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.

2
0
FILES
journal-skills/
skill.md
scripts
pubmed_journal_recommender.py
references
guide.md
assets
journal_recommendation_template.csv
86100Total Score
View Evaluation Report
Core Capability
87 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
8 / 8
Agent Usability
14 / 16
Human Usability
8 / 8
Security
9 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
15 / 20 Passed
86You have a manuscript title/abstract and need a shortlist of suitable journals to submit to
3/4
86You want evidence-based journal matching using similar PubMed literature and journal distribution statistics
3/4
86Topic- and abstract-driven journal recommendation workflow
3/4
86PubMed-based similar literature search and journal frequency distribution compilation
3/4
86End-to-end case for Topic- and abstract-driven journal recommendation workflow
3/4

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)
    • EMAIL required (per NCBI policy)
    • API_KEY optional (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

  1. Topic and Scope Definition

    • Identify the research field, subfield, and intended readership.
    • Confirm journal type preferences and constraints (e.g., OA mandates).
  2. 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.
  3. 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.
  4. Recommendation Output

    • Provide a ranked list with:
      • Fit rationale (topic alignment + similarity evidence)
      • Risks (scope mismatch, policy conflicts, timeline concerns)
      • Alternatives (backup journals)

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.csv to ensure consistent fields and downstream usability.