clinical-decision-support
This Claude Code skill generates professional clinical decision support documents for pharmaceutical and clinical research settings, producing publication-ready LaTeX/PDF outputs. Use it to create patient cohort analyses with biomarker stratification and statistical comparisons, or treatment recommendation reports with GRADE evidence grading and clinical decision algorithms. It supports survival analysis, efficacy visualization, and regulatory-compliant evidence synthesis for drug development and guideline development, but not individual bedside treatment plans.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/clinical-decision-support && cp -r /tmp/clinical-decision-support/skills/clinical-decision-support ~/.claude/skills/clinical-decision-supportSKILL.md
# Clinical Decision Support Documents ## Description Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development: 1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development. **Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings. **Writing Style:** For publication-ready documents targeting medical journals, consult the **venue-templates** skill's `medical_journal_styles.md` for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance. ## Capabilities ### Document Types **Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence) **Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies ### Clinical Features - **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions ## Pharmaceutical and Research Use Cases This skill is specifically designed for pharmaceutical and clinical research applications: **Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries **Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals **Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics **Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup ## When to Use Use this skill when you need to: - **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing **Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill) ## Visual Enhancement with Scientific Schematics **⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.** This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, t
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.