Skill UI

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.

A robust, on-device solution for processing large-scale clinical text corpora. Supports batch NER, PII extraction, and de-identification over thousands of notes. Features include sharding, checkpointing, resumable functionality, and streaming results to JSONL, ensuring PHI safety and constant memory usage for massive datasets.

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.

This skill de-identifies sensitive PII from clinical notes across numerous languages, including Spanish, German, French, and Arabic. It automatically selects language-specific PII models, national ID regex patterns, and generates locale-aware fake surrogates. Designed for global healthcare datasets, it runs entirely on-device, ensuring maximum privacy and linguistic accuracy for diverse international clinical records.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.

This skill enables the execution of OpenSquilla agents against complex, real-world software engineering benchmarks (SWE-bench). It runs the entire process—from pulling the necessary Docker image to collecting the patch and running official evaluations—in a containerized environment. Use it to test an agent's ability to solve specific coding issues, benchmark performance across datasets (e.g., verified, multilingual), or verify if a generated patch successfully resolves an instance.

DataLad manages scientific datasets using Git and git-annex, handling large files efficiently by storing content remotely. It captures computational provenance for reproducible analysis, allowing users to clone, fetch, and publish datasets from repositories like OpenNeuro. Ideal for managing large data files and ensuring analysis reproducibility.

Compare multiple Japanese NLP libraries, models, or datasets across selected criteria using a comparison table. Supports both specific tool comparison (seed mode) and topic-based comparison (topic mode). Uses bundled data and web search to find relevant resources. Ideal for evaluating alternatives before choosing the best tool for your project.

Discover Japanese NLP resources including existing ones in awesome-japanese-nlp-resources and new unlisted resources. Mines bundled datasets and expands via web research across GitHub and Hugging Face. Find alternatives, equivalents, or prepare contributions for Japanese NLP tools and models.

Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Covers prompt-level unit evals, tool-calling checks, end-to-end tasks, safety/adversarial tests, and LLM-as-judge scoring to gate deployments on quality.

Generate output schema files (dataset_schema.json, output_schema.json, key_value_store_schema.json) for Apify Actors by analyzing source code. Helps create or update Actor output schemas with proper field definitions, nullable settings, and display views. Follows existing repository conventions and reuses type definitions.