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Precision Oncology
Browse reusable instructions for your AI tools. Copy and customize a prompt, or download a skill for a supported agent.

Precision Oncology
Extends a mechanistic or association-level biomedical finding into a staged validation pathway that moves from descriptive evidence toward stronger functional support, mechanistic specificity, and clinical relevance. Use this skill when a user has a pathway, biomarker, cell-state, target, mechani...
AI-powered multimodal fusion of radiology (CT/MRI/PET) and pathology (H&E/IHC) imaging with clinical and genomic data for comprehensive cancer diagnostics and treatment prediction.
Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.
Search medRxiv medical preprints with natural language queries. Powered by Valyu semantic search.
Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent labels, padding, and publication-ready DPI.
Generates a structured prompt framework for clinical study protocols. Supports Diagnostic, Efficacy, Etiology, and Prognosis studies. Calculates sample size and provides logic guides for LLMs.
Multi-agent research literature analysis
Disease-Specific Compound Screening - Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness. Use this skill for drug discovery tasks involving calculate dleps score pred molecule admet calculate mol drug chemistry get compound by name. Combine...
Calculates and harmonizes Tumor Mutational Burden (TMB) across platforms to predict immunotherapy response.
Use this skill when stuck in circular debugging, when solutions aren't working despite multiple attempts, or when the user expresses frustration with lack of progress. Bring in GPT-5 as a third-party consultant to provide fresh perspective on complex technical problems, architectural decisions, o...
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Manual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal in...
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
Generate scientific code, protocols, and domain-specific text with quality control
Meta-skill for improving and optimizing prompts using Anthropic's prompt engineering best practices. Provides the 4-step improvement workflow (example identification, initial draft, chain of thought refinement, example enhancement), keyword registries for documentation lookup, and decision trees ...
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating...
Expert guide for crafting high-quality, production-grade prompts and system prompts for LLMs. Use when a user wants to create, improve, debug, or evaluate a prompt — whether for Claude, GPT, or any other LLM. Triggers on create prompt, write prompt, improve prompt, system prompt, prompt engineeri...
Systematic prompt engineering methods for AI-assisted academic research workf...
Deep research agent searching 10+ sources with local or cloud LLMs
Create reproducible research workflows with R and RMarkdown/Quarto
Process large documents (200+ pages) with structure preservation, intelligent parsing, and memory-efficient handling. Use when working with complex formatted documents, multi-level hierarchies, or when you need to extract structured data from large files like PDFs, DOCX, or text files.
Synthesize invariant principles from 3+ sources — find the core that survives across all expressions.
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Explore all resourcesResource formats: Prompts are ready to copy into a conversation. Skills install into the compatible agent shown on their page.
How it worksInclude the instructions, intended platforms, author, and license. Submissions are reviewed before publication.