--- name: journal-skills description: 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. license: MIT author: AIPOCH --- > **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) ## 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 ```bash 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. ## When Not to Use - Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first. - Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences. - Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope. ## Required Inputs | Field | Required | Format/Source | Example | If Missing | |---|---|---|---|---| | User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide | | Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing | | Output preference | No | Text | Language, format, target journal, template | Use skill default format | ## Output Contract - Primary output: Structured result or target file aligned with this skill's objective. - Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths. - Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format. - If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain. ## Failure Handling - Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause. - Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade. - Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps. ## User Checkpoints - Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user. - Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user. ## Input Validation This skill accepts requests that match the documented purpose of `journal-skills` and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: > `journal-skills` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. ## Quick Validation - Check that key scripts, templates, or reference file paths this skill depends on exist. - Check that the final output contains the core fields, sections, or files specified for this task. - Check that results clearly mark assumptions, limitations, and incomplete items. --- Source attribution: aipoch Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Evidence%20Insight/journal-skills/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 AIpoch Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: aipoch Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Evidence%20Insight/journal-skills/SKILL.md Attribution basis: Source repository record Fields of work: Education & Research, Healthcare & Life Sciences, Marketing, Product Management Roles: Academic Writer, Content Marketer, Medical Writer, Product Manager, Researcher Specialties: Specialty not specified Tools: Pubmed, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 12a15867e73847cc2e84ad9683f22b5b0153c83c3c6dd63f16b3f140de98be86