Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.
Run this command in Claude Code to install the skill
/install https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pptx-postersInstall to your personal skills directory (~/.claude/skills/pptx-posters/)
# Create skill directory
mkdir -p ~/.claude/skills/pptx-posters
# Download SKILL.md from GitHub
curl -sL "https://raw.githubusercontent.com/K-Dense-AI/claude-scientific-skills/main/scientific-skills/pptx-posters/SKILL.md" \
-o ~/.claude/skills/pptx-posters/SKILL.mdTarget: ~/.claude/skills/pptx-posters/
Scientific & Research
Skills for scientific computing, bioinformatics, and research
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.