Discover Agent Skills for data science & ml. Browse 61skills for Claude, ChatGPT & Codex.
Evaluates AI systems for fairness and implements mitigation strategies using demographic parity, equalized odds, and proxy detection.
Designs, evaluates, and optimizes high-performance LLM prompts using systematic engineering patterns and rigorous testing frameworks.
Manages GPU VRAM allocation through OOM retry logic, idle auto-unloading, and cross-service signaling protocols.
Accesses and analyzes global public statistical data from the Data Commons knowledge graph for research and development.
Analyzes single-cell omics data using deep generative models and probabilistic frameworks for genomics research.
Implements Group Relative Policy Optimization (GRPO) for training language models in reasoning, logic, and structured output tasks.
Implements robust Retrieval-Augmented Generation systems using vector databases and semantic search to ground AI responses in external knowledge.
Evaluates research rigor by assessing methodology, experimental design, and statistical validity using frameworks like GRADE and Cochrane.
Performs advanced computational molecular biology tasks including sequence analysis, database queries, and structural bioinformatics.
Simplifies building and managing stateful AI agents with long-term memory using the Letta framework.
Accesses and analyzes data from the Human Metabolome Database for metabolite identification, biomarker discovery, and clinical research.
Provides programmatic access to over 40 bioinformatics web services and databases for streamlined biological data retrieval and analysis.
Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the Astropy Python library.
Guides developers in choosing the optimal neural network architecture based on data modality, problem constraints, and performance requirements.
Performs comprehensive differential gene expression analysis on bulk RNA-seq data using the Python implementation of DESeq2.
Diagnoses machine learning training issues and routes users to specific optimization strategies based on model symptoms.
Routes AI and machine learning tasks to specialized Yzmir engineering packs based on specific project requirements and technical domains.
Optimizes large-scale deep learning workflows using Fully Sharded Data Parallel (FSDP) techniques in PyTorch.
Accesses and retrieves gene expression data from the NCBI Gene Expression Omnibus (GEO) for advanced transcriptomics and genomic analysis.
Performs systematic, objective technical analysis of weekly price charts to identify trends, support levels, and probabilistic price scenarios.
Generates professional-grade scientific plots and data visualizations using Python's foundational plotting library.
Automates querying the Reactome pathway database for gene enrichment, molecular interactions, and systems biology research.
Provides comprehensive molecular analysis and manipulation capabilities for cheminformatics and drug discovery workflows.
Processes, filters, and analyzes mass spectrometry data using the matchms Python library for metabolomics and chemical discovery.
Streamlines the creation of distributable scientific Python packages using modern pyproject.toml standards and community-best practices.
Automates end-to-end scientific research workflows from initial data analysis and hypothesis generation to producing publication-ready LaTeX manuscripts.
Builds production-grade multi-agent systems, AgentOS runtimes, and complex agentic workflows with native MCP integration.
Synchronizes MetaTrader 5 and Python environments to automate market data exports and translate MQL5 indicators into validated Python code.
Automates scientific hypothesis generation and testing by synthesizing observational data with research literature using LLMs.
Extends Transformer model context windows using RoPE, YaRN, and ALiBi techniques for processing massive documents and datasets.
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