Skill UI

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

This skill acts as an expert data quality engineer to systematically assess dataset health before analysis or model training. It profiles data using specialized scripts to detect missing values, outliers, duplicates, and distribution shifts. The tool generates a Data Quality Score (DQS) and provides prioritized remediation plans to prevent data issues from corrupting downstream models or dashboards.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

Provides a systematic guide for diagnosing and improving poor search quality in vector databases like Qdrant. This guide covers troubleshooting issues such as low recall, when approximate search degrades, selecting optimal embedding models, and correctly tuning HNSW parameters, quantization, and filtering strategies. Essential for optimizing RAG and semantic search pipelines.

Guidance for designing, building, refreshing, and reviewing Power BI / Analysis Services tabular models from the terminal. Covers star schema, relationships, measures, calculation groups, roles, storage modes, incremental refresh, and model optimization. Routes every operation to the narrowest capable tool: te CLI first, then TOM or MCP, then TMDL. Ensures model quality, performance, and readiness for Copilot/AI.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

This guide helps users transform weak research ideas, correlations, or policy descriptions into strong, internationally generalizable, and causally identifiable research questions suitable for top-tier academic journals. It emphasizes establishing exogenous variation, clear underlying mechanisms, and structural modeling potential to elevate research quality.

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.