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huggingface / skills-hugging-face-datasets exact

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work...

dkyazzentwatwa / chatgpt-skills-dataset-comparer exact

Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.

Arize-ai / phoenix-phoenix-cli exact

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with...

vaex 0.14
ovachiever / droid-tings-vaex exact

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast...

vaex 0.14
K-Dense-AI / claude-scientific-skills-vaex exact

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast...

markpitt / claude-skills-fine-tuning-data-generator exact

Generates comprehensive synthetic fine-tuning datasets in ChatML format (JSONL) for use with Unsloth, Axolotl, and similar training frameworks. Gathers requirements, creates datasets with diverse...

K-Dense-AI / claude-scientific-skills-deepchem exact

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and...

K-Dense-AI / claude-scientific-skills-polars exact

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for...

mudigosa / image-classifier exact

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do...

Arize-ai / phoenix-phoenix-evals exact

Build and run evaluators for AI/LLM applications using Phoenix.

eddiebe147 / claude-settings-data-analyzer exact

Advanced data analysis, pattern detection, and insight generation from structured and unstructured datasets

armelhbobdad / opik-skills-opik-eval exact

Create and run evaluations on your LLM outputs. Use when testing prompts, measuring quality, comparing models, or creating evaluation datasets.

ngxtm / devkit-fine-tuning-expert exact

Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.

Jeffallan / claude-skills-fine-tuning-expert exact

Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.

pytdc 0.09
ovachiever / droid-tings-pytdc exact

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

pytdc 0.09
K-Dense-AI / claude-scientific-skills-pytdc exact

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

ovachiever / droid-tings-omero-integration exact

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

K-Dense-AI / claude-scientific-skills-omero-integration exact

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

dask 0.09
ovachiever / droid-tings-dask exact

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.