Results for “dataset”

Manage datasets, tables, and jobs in BigQuery. Run SQL queries, manage BigQuery resources, and perform basic data ingestion and analysis.

Create publication-quality statistical graphics from tabular datasets with minimal code, supporting multivariate analysis, statistical estimation, and complex multi-panel figures.

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.

Runs Apify cloud actors for structured web scraping and exports datasets to S3, with input schema validation and webhook notifications.

Organize, query, validate, and convert neuroscience datasets following the Brain Imaging Data Structure (BIDS) standard, including metadata sidecars and derivatives.

Create publication-quality statistical graphics in Python with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and multi-panel figures.

Data analysis workflow from import through modeling and communication. Use when analyzing a dataset, exploring data, building a statistical model, selecting features, or communicating findings to stakeholders.

Use to check whether a conversation export, dataset or AI pipeline is leaking personal data that should have been redacted, and to measure how well the redaction actually works. Trigger for "is this export safe to share", "check our redaction", "can we use support transcripts for training", sending transcripts to a vendor or model, "is there PII in this dataset", or before opening support data to a wider audience.

Create publication-quality statistical graphics using Seaborn, with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

Manage golden dataset regression tests for LLM prompts using Promptfoo. Includes setup, test case capture, repeatable runs, and reports. Use after changing prompts or when checking for prompt regressions.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Analiza datasets con Pandas y NumPy: explora distribuciones, correlaciones y patrones, y aplica tests de hipótesis para extraer conocimiento no obvio.

Legal contract analysis using CUAD dataset (41 risk categories). Supports NDA, SaaS, M&A, employment, payment/merchant, and finder/broker agreements. Identifies red flags, suggests redlines, compares to market standards.

Runs a three-phase AOI training pipeline: AutoML HPO baseline, DEFT iterative data improvement, and AutoML refinement on the augmented dataset.

Load sharded datasets (npy, Parquet, HDF5, raw binary) into distributed cuPyNumeric arrays using manual partitioning and Legate task launches.

Routes NuRec/Neural Reconstruction requests to the correct upstream NVIDIA skill (datasets, conversion, training, rendering, object harvesting, frame cleanup).

Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.

Fine-tune instruction-following LLMs with Unsloth's optimized SFTTrainer, covering dataset formatting, chat templates, training configuration, and thinking-model patterns.

Executes MySQL database development and review tasks based on user-provided rules, covering naming constraints, index design, and data archiving for large datasets.

Process and analyze tabular datasets larger than RAM using lazy, out-of-core DataFrames, with fast aggregations, visualization, and machine learning integration.

Read, write, and analyze genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

Runs R statistics analyses on local datasets, producing NLSS-format tables, narratives, and JSONL logs from CSV, SAV, RDS, RData, or Parquet files.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.