Skill UI

This tool serves as an interactive, step-by-step installer for the Everything Claude Code (ECC) framework. It guides users in selecting and installing complex development skills (covering patterns for Django, Spring Boot, Python, Go, etc.) and standardized rulesets (e.g., common best practices, language-specific guides). It ensures the correct setup, validation, and optimization of the development environment for both user and project levels.

Guides use of the Figma MCP server to pull design context, screenshots, variables, and assets, then convert nodes into production-ready code following project conventions and validation steps.

Automates clustering workflows on supplied datasets with K-means, DBSCAN, or hierarchical algorithms, handling preprocessing, validation, metrics, and visualizations to expose groupings and anomalies.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

Use this skill to perform read-only API calls for exploring, previewing, searching, and filtering structured datasets hosted on Hugging Face. It allows users to validate dataset availability, list splits, paginate large datasets, and retrieve comprehensive metadata and statistics, making it ideal for data validation and content extraction workflows.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

The HypoGeniC skill facilitates the iterative proposal and scoring of textual hypotheses from labeled datasets using LLMs. It provides tools for generating candidate hypothesis banks and task-prediction statistics. Crucially, it mandates a rigorous, local review workflow, emphasizing dataset auditing (checksums, schemas), configuration validation, and explicitly separating candidate results from definitive scientific conclusions.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

This comprehensive guide details the necessary procedures for migrating and validating Apple Notes automation scripts across major macOS versions. It provides a compatibility matrix, mandatory pre-upgrade backup scripts, and post-upgrade validation checks (including TCC permission resets and function verification) to ensure scripts maintain stability and functionality after OS updates.

This guide sets up robust CI/CD pipelines for validating Attio integrations. It combines mocked unit tests (using MSW) for speed and gated live API integration tests for pre-release validation. Use this to ensure complete code reliability and automated quality checks across all development pushes.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).