Transform lengthy academic papers into concise, structured 250-word abstracts.
The English summary is being prepared. Showing the original description.
Generates the "Risk of Bias" results section for a meta-analysis based on assessment tables and statistics. Use when the user wants to draft the risk of bias analysis text from provided data tables.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
scripts/format_result.py plus 1 additional script(s).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.cd "20260316/scientific-skills/Academic Writing/meta-results-risk-of-bias"
python -m py_compile scripts/format_result.py
python scripts/format_result.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/format_result.py with the validated inputs.See ## Workflow above for related details.
scripts/format_result.py with additional helper scripts under scripts/.Run this minimal command first to verify the supported execution path:
python scripts/format_result.py --help
This skill generates a professional, academic "Risk of Bias" results section for a meta-analysis. It analyzes the provided statistics and detailed assessment table, drafts the text using a clinical expert persona, and automatically formats the output with necessary figure citations.
Use this skill when the user provides:
And asks for:
(Figure 1) before the last punctuation and append the figure caption.Use the following prompt to generate the initial text:
Role: Clinical Medical Expert
Task: Write an academic "Results" section based on the following inputs:
Requirements:
Run the formatting script to insert the figure citation and caption.
python scripts/format_result.py --text "<generated_text>" --language "{{language}}"
scripts/format_result.py: Inserts (Figure 1) and appends the figure placeholder and caption.meta_results_risk_of_bias_result.md unless the skill documentation defines a better convention.This skill accepts requests that match the documented purpose of meta-results-risk-of-bias 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:
meta-results-risk-of-biasonly handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Run this minimal verification path before full execution when possible:
python scripts/format_result.py --help
Expected output format:
Result file: meta_results_risk_of_bias_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Transform lengthy academic papers into concise, structured 250-word abstracts.
The English summary is being prepared. Showing the original description.
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
The English summary is being prepared. Showing the original description.
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
The English summary is being prepared. Showing the original description.
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
The English summary is being prepared. Showing the original description.
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
The English summary is being prepared. Showing the original description.
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
The English summary is being prepared. Showing the original description.