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Find your next superpower If you are an agent, refer to our llms.txt for full access."..." for exact phrases · Try typography, debugging or spreadsheetGuides the design of scalable SQL and NoSQL database schemas. Covers normalization, indexing strategies, migration patterns, and constraints to support data integrity, query performance, and maintainable data models.
Точка входа в Task Master workflow для цели по данным (роль database). Use when цель по данным многошаговая и нужен цикл задач до готового; точечное — $database-query/$database-schema/$database-migrate/$database-optimize/$database-cache.
Acts as the entry point for Databricks CLI tasks, including authentication, profiles, and bundles. Routes users to matching product skills and directs data exploration and SQL generation to the data-discovery skill.
Identifies downstream tables, dashboards, and processes affected by broken, stale, modified, or deleted BigQuery assets. Supports dependency and impact assessment, including the consequences of pausing updates.
Explains upstream and downstream flows in Google Cloud Data Lineage graphs for BigQuery and Cloud Storage. Produces readable Markdown reports to clarify provenance and debug data quality issues, excluding downstream blast-radius analysis.
Supports SMILES parsing, molecular standardization, descriptors, fingerprints, clustering, and 3D conformers. Provides sensible defaults and parallel processing while returning native RDKit molecule objects.
Provides a preprocessing pipeline for raw credit data, including formatting, abnormal-period filtering, missing-value analysis, and variable screening. Filters variables using missingness, IV, PSI, Null Importance, and correlation, then generates a cleaning report.
Extracts, normalizes, and standardizes financial data from CIMs, offering memorandums, SEC filings, web searches, or MCP servers. Produces investment committee-ready Excel workbooks with consistent formatting and documented assumptions for due diligence, private equity analysis, and portfolio reporting.
Discovers tables, resources, column definitions, and usage warnings in datapackage.json metadata. Explains efficient querying and loading of associated Parquet, DuckDB, SQLite, and CSV data.
Convert raw data into training formats, apply chat templates, and configure sequence packing. Covers synthetic training data generation and dataset cards.
Uses standardized plugin settings schemas to generate ready-to-use Grafana data-source configuration. Supports provisioning plugins as code through YAML or Terraform.
Generates production code for the Dataverse SDK using advanced patterns. Includes error handling and optimization techniques.
A growing collection of real, public skills. Descriptions and instructions indexed 25 Sept 2026.
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