--- name: research-article-weekly description: Generates a weekly academic literature report based on keywords using PubMed. Use when the user wants to track recent research progress on a specific topic, automatically retrieving, classifying, and summarizing relevant papers from the last 7 days. license: MIT author: AIPOCH --- > **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) # Research Article Weekly This skill generates a weekly report of academic literature for a given keyword. It searches PubMed for articles published in the last 7 days, classifies them into generic research categories (Fundamental, Applied, Methodology, Review, Other), and produces a summarized report. This tool is domain-agnostic and adapts to any research field indexed in PubMed. ## When to Use - Use this skill when you need generates a weekly academic literature report based on keywords using pubmed. use when the user wants to track recent research progress on a specific topic, automatically retrieving, classifying, and summarizing relevant papers from the last 7 days in a reproducible workflow. - Use this skill when a evidence insight task needs a packaged method instead of ad-hoc freeform output. - Use this skill when the user expects a concrete deliverable, validation step, or file-based result. - Use this skill when `scripts/pubmed_search.py` is the most direct path to complete the request. - Use this skill when you need the `research-article-weekly` package behavior rather than a generic answer. ## Key Features - Scope-focused workflow aligned to: Generates a weekly academic literature report based on keywords using PubMed. Use when the user wants to track recent research progress on a specific topic, automatically retrieving, classifying, and summarizing relevant papers from the last 7 days. - Packaged executable path(s): `scripts/pubmed_search.py`. - Structured execution path designed to keep outputs consistent and reviewable. ## Dependencies - `Python`: `3.10+`. Repository baseline for current packaged skills. - `Third-party packages`: `not explicitly version-pinned in this skill package`. Add pinned versions if this skill needs stricter environment control. ## Example Usage ```bash cd "20260316/scientific-skills/Evidence Insight/research-article-weekly" python -m py_compile scripts/pubmed_search.py python scripts/pubmed_search.py --help ``` Example run plan: 1. Confirm the user input, output path, and any required config values. 2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings. 3. Run `python scripts/pubmed_search.py` with the validated inputs. 4. Review the generated output and return the final artifact with any assumptions called out. ## Implementation Details See `## Workflow` above for related details. - Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. - Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. - Primary implementation surface: `scripts/pubmed_search.py`. - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. - Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. ## Inputs - `keywords`: The search term(s) for the literature (e.g., "lung cancer", "CRISPR", "machine learning", "climate change"). ## Workflow 1. **Search & Generate Draft Report**: The skill executes the bundled Python script to search PubMed and generate a draft Markdown report using rule-based classification. This provides an immediate, usable output even without LLM processing. ```bash python scripts/pubmed_search.py --keywords "{keywords}" --days 7 --limit 20 --format markdown ``` 2. **Refine Report (Optional - AI Enhanced)**: If an AI environment is available, the Agent can take the raw JSON output (by running with `--format json`) or the draft Markdown report and refine it using the advanced logic below for better summarization and topic extraction. **Classification Logic (for AI Refinement):** For each retrieved article (Title + Abstract), classify it using the following logic: **System Prompt:** > Act as a versatile research analyst. Your task is to categorize the research article based on its title and abstract in relation to the user's keyword: "{keywords}". > > **Research Types:** > 1. **Fundamental Research**: Theoretical studies, mechanisms, basic science, discovery, or foundational work. > 2. **Applied Research**: Practical applications, clinical trials, engineering implementations, case studies, or field deployments. > 3. **Methodology & Tools**: New algorithms, techniques, software, instruments, or experimental frameworks. > 4. **Review & Survey**: Literature reviews, meta-analyses, systematic reviews, or perspectives. > 5. **Other**: Education, policy, news, editorials, or papers that do not fit the above. > 6. **Irrelevant**: Not related to the keyword. > > **Task:** > 1. Assign the most appropriate **Research Type** from the list above. > 2. Extract a specific **Topic Tag** (1-3 words) representing the core subject (e.g., "Deep Learning", "Gene Editing", "Market Analysis"). > > **Output Format:** > Return a valid JSON object: `{"type": "Research Type Name", "topic": "Topic Tag"}`. Do not output anything else. 3. **Generate Final Report (for AI Refinement)**: Group the articles by their assigned **Research Type**. For each type that contains articles, generate a summary section. **System Prompt:** > Act as a comprehensive research summarizer compiling a "Weekly Research Update". > > **Input:** A list of research papers (Title, Journal, Abstract, Topic Tag) belonging to the Research Type: "{category}". > > **Task:** Write a concise, engaging summary for this research type. > - **Synthesize**: Group papers with similar **Topic Tags** and summarize their collective contribution. > - **Highlight**: Identify the most significant findings or innovations. > - **Tone**: Professional, objective, and adapted to the specific domain of the papers (e.g., formal for physics, analytical for social science). Avoid generic "excitement" unless warranted by a major breakthrough. > - **Reference**: List the papers with their Titles and Journals. > > **Format:** > ### {Category Name} > [General Summary Paragraph highlighting key themes] > > **Key Updates:** > - **[Topic Tag]**: [Summary of findings from related papers]. *Refs: [Title] (Journal)* > - ... > > (If a paper stands alone, list it individually) 4. **Final Output**: Combine all sections into a single Markdown document titled "Weekly Research Report: {keywords}". Add a brief "Executive Summary" at the top highlighting the distribution of papers (e.g., "This week saw a focus on Applied Research in [Topic]..."). ## Quality Rules - **Source**: Must use real data returned from the `pubmed_search.py` script. Do not hallucinate papers. - **Coverage**: Ensure all retrieved and relevant papers are included in the report. - **Tone**: Objective, informative, and structured. Avoid overly sensational language. - **Error Handling**: If the script returns no results, output "No significant research articles found for '{keywords}' in the last 7 days." --- Source attribution: aipoch Original project: https://github.com/aipoch/medical-research-skills/blob/686e09dfdb45d9fa1a08664f06bb93769b3c9d91/scientific-skills/Evidence%20Insight/research-article-weekly/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/research-article-weekly/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research Roles: Academic Writer, Data Analyst, Researcher Specialties: API Tools: Pubmed Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 90b5c3cb8f6b0b67adabf80ab47a9933373a834bd29b5efef9bd197384af2251