pyhealth
Comprehensive Python healthcare AI toolkit for clinical data processing, medical coding translation, and developing deep learning models like RETAIN and Transformers for EHR, physiological signals, and clinical prediction tasks.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
341 skills found
Comprehensive Python healthcare AI toolkit for clinical data processing, medical coding translation, and developing deep learning models like RETAIN and Transformers for EHR, physiological signals, and clinical prediction tasks.
Audit outbound network requests and detect data exfiltration patterns in OpenClaw skills to ensure secure outbound communication.
Social media intelligence gathering for TikTok and Instagram. Discover trending hooks, analyze creator strategies, and perform profile data research using the ScrapeCreators API.
Full-stack application orchestrator that analyzes natural language requests to determine tech stacks, scaffold projects, and coordinate specialized development agents.
Systematic performance engineering: baseline measurement, profiling, bottleneck diagnosis, and evidence-based optimization guidance for high-performance applications.
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Search the live web using Baidu AI Search Engine (BDSE) for real-time information, documentation, and research topics.
Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.
Build AI agents with tool calling and multi-step reasoning. Generate, manage, and orchestrate custom skill files for Claude Code, Cursor, Cline, and other AI assistants to standardize your development workflows.
Meta-skill for generating publication-ready scientific figures, multi-panel layouts, and journal-compliant visualizations using Python's matplotlib, seaborn, and plotly libraries.
Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.
Fetch and analyze current trending programming models from OpenRouter. Ideal for selecting models for reviews, optimizing AI costs, and staying updated on AI coding trends with real-time pricing and context window data.