数据分析、机器学习、LLM 应用、提示词设计（9144 个）
Create and refine Jupyter notebooks for experiments, exploratory analysis, or tutorials. Bundled templates help organize runnable code and short explanations into clear steps, while validation guidance supports notebooks that others can read and rerun.
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Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.
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Explore scientific data across more than 200 file formats to understand its structure, contents, and quality. Produce Markdown reports with statistical summaries, format-specific findings, and recommendations for preprocessing, visualization, and further analysis.
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Choose embedding models, which turn text into numerical representations of meaning, for semantic search and AI answers grounded in retrieved documents. Includes examples for document splitting, preprocessing, multilingual content, and measuring retrieval quality.
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Search NCBI GEO for gene expression studies, retrieve experiment details and expression tables, and prepare public datasets for analysis. Includes workflows for data quality checks, comparisons between sample groups, and expression summaries across studies.
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Develop and review machine learning approaches through at least three rounds of critique and refinement. Compare models with simple reference predictions, question data assumptions, and investigate overfitting, feature choices, and evaluation methods before accepting a solution.
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Analyze genomic regions with gtars using Python or command-line tools. Find overlaps, generate coverage tracks showing read density, prepare regions as tokens for machine learning, and split single-cell fragment data for further analysis.
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Prepare pathology image datasets by detecting tissue in whole slide images and extracting smaller image tiles. Use random, grid, or score-based selection, preview extraction locations, and apply preprocessing filters to review and prepare the resulting tiles.
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Evaluate trained machine learning models using metrics suited to the task. Compare candidates consistently, review decision thresholds and probability estimates, and identify weaknesses in validation before deployment or publication.
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Find and download public cancer imaging datasets from NCI Imaging Data Commons. Filter scans and pathology images by metadata, preview them in a browser, check licenses, and generate source citations for research or AI training.
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Explain an existing machine learning model by summarizing influential input features and examining individual predictions. It helps investigate unexpected feature interactions and communicate model behavior to nontechnical stakeholders, while noting limitations and potentially misleading explanations.
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Build and deploy biological data analysis pipelines on Latch using Python, Nextflow, or Snakemake. The skill also guides cloud file management, sample tracking, and CPU, memory, and GPU settings for individual processing tasks.
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Work with geographic points, lines, and areas in Python. Read spatial files, combine datasets by location, calculate areas and distances, transform coordinate systems, and export results as maps or converted files.
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Work with DNA, RNA, and protein sequences in Python. Read and convert biological files, compare sequences, search for similar sequences with BLAST, retrieve NCBI records, analyze protein structures, and build or visualize evolutionary trees.
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Design and implement data visualizations that explain patterns through clear charts, purposeful color, and a narrative. Choose suitable scales, check data sources, and add interactions that let viewers explore details.
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Investigate unusual transactions, sensor spikes, rare failures, and other suspicious records. The skill helps compare anomaly detection methods and thresholds, prepare records for human review, and plan checks for false alarms and missed anomalies.
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Retrieve population, economic, health, and environmental statistics through the Data Commons Python API. Resolve place names into query identifiers, compare regions, explore historical trends, and convert results into Pandas tables for further analysis.
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Build interactive charts, networks, timelines, and maps with D3.js, Chart.js, or custom SVG graphics. The skill guides data preparation, scale selection, accessible colors, and storytelling so viewers can explore patterns without misleading visual proportions.
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Process datasets that exceed available memory and run Python computations across multiple cores or machines. The skill guides large table and array operations, multi-file cleaning and aggregation, custom task dependencies, and execution settings for parallel workflows.
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Retrieve stock prices, exchange rates, cryptocurrency prices, company financial statements, and economic indicators through the Alpha Vantage API. It also covers historical data, news sentiment, and technical indicators, which summarize price and trading patterns.
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Create charts from existing CSV files, tables, or query results to show trends, distributions, comparisons, and relationships. The skill guides chart selection and axis choices for everyday analysis, presentation slides, and simple dashboards.
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Build and run genomics analyses on DNAnexus, a cloud platform for biomedical data. Covers analysis apps, file organization, multi-step workflows, job monitoring, and automation with the dxpy Python library.
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Build automated data checks with Great Expectations and dbt to detect missing values, duplicates, invalid ranges, and stale records. Define data contracts, shared agreements on data structure and quality, and summarize validation results across tables.
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Turn analytical findings into reports and presentations that explain what the numbers mean and what to do next. Structure the narrative, choose clear visual comparisons, highlight key findings, and communicate uncertainty to audiences without technical expertise.
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