Rank journals for manuscript submission using a local AJG dataset.
npx skills add https://github.com/canyangliunian/Agent-skills --skill abs-journal
Please help me install this Agent Skill. Skill: ABS-Journal Source: https://github.com/canyangliunian/Agent-skills/tree/main/ABS-Journal Command: npx skills add https://github.com/canyangliunian/Agent-skills --skill abs-journal
ABS-Journal helps researchers identify suitable publication venues for manuscripts by leveraging a local Chartered ABS AJG dataset, enabling offline, deterministic journal recommendations and optional data updates.
Run the hybrid workflow with your paper title to generate local AJG-based journal recommendations.
High-intent search queries and answers about installing and using this skill.
The process enforces easy, medium, and hard gating levels alongside a 1:1 rating balance to ensure diverse journal recommendations. This filtering mechanism prevents an over-concentration of suggestions in any single rating tier.
Yes, the local AJG dataset can be refreshed when updates are needed. The offline-first design relies on a specific CSV file within the assets directory, which can be replaced or updated to maintain current journal ranking data.
Yes, hybrid AI journal recommendation works entirely offline by combining local AJG data with an optional internal AI filtering pass. This approach improves match quality for your manuscript without relying on external API calls or network access.
You can shortlist target journals by running the hybrid workflow with your paper title, which matches your research against local AJG candidate pools. It outputs a detailed Markdown report containing the journal name, Field, and rationale for each recommendation.
Offline journal recommendation results are provided in a detailed Markdown report. This output format includes the ranked list of journals, their respective fields, and the rationale behind each suggestion based on the local AJG dataset.
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