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SkillsMP hat 93 Skills aus databricks-solutions/vibe-coding-workshop-template gesammelt. Öffne einen Skill, um Quelle und Details zu prüfen.
databricks-solutions/vibe-coding-workshop-templateEs werden 13 von 93 gesammelten Skills angezeigt.
Use when adding user, session, environment, or version context to MLflow traces — Python or TypeScript. Covers the reserved metadata fields (`mlflow.trace.user`, `mlflow.trace.session`), auto-populated environment metadata (`mlflow.source.*`, Git provenance,…
Quellsprache: Englisch
Use after evaluation (Step 4) passes but BEFORE deployment (Step 6). Covers translating technical eval metrics to business-meaningful terms, running a structured review with business and compliance stakeholders, capturing sign-off, and blocking promotion if…
Quellsprache: Englisch
Guides the creation of new Agent Skills following the AgentSkills.io specification. Covers required frontmatter (name, description), optional metadata, directory structure (SKILL.md, references/, scripts/, assets/), progressive disclosure principles, and…
Quellsprache: Englisch
Grain definition patterns for fact tables during the Gold layer design phase. Use when choosing grain types (transaction, aggregated, snapshot), documenting grain in YAML schemas, and applying the PK-grain decision tree to determine whether a fact table needs…
Quellsprache: Englisch
Dimension design patterns for Gold layer modeling. Covers role-playing dimensions, degenerate dimensions, junk dimensions, mini-dimensions, outrigger dimensions, denormalized hierarchies, null handling, and flags as textual attributes. Use when designing…
Quellsprache: Englisch
Advanced fact table design patterns for Gold layer modeling. Covers measure additivity classification (additive, semi-additive, non-additive), factless fact tables, accumulating snapshot facts, consolidated fact tables, header/line fact patterns,…
Quellsprache: Englisch
Enterprise integration patterns for Gold layer dimensional models. Covers conformed dimensions, the enterprise data warehouse bus matrix, shrunken/rollup dimensions, conformed facts, and drill-across query patterns. Use when planning dimensions shared across…
Quellsprache: Englisch
Patterns for creating clean, professional Mermaid ERD diagrams for data modeling documentation. Use when documenting Gold layer data models, creating master ERDs for complete models, domain-specific ERDs for focused views, or summary ERDs for large models…
Quellsprache: Englisch
Comprehensive documentation standards for Gold layer tables including naming conventions, column descriptions, and metadata requirements. Use when creating Gold layer tables, columns, or writing documentation to ensure dual-purpose descriptions that serve…
Quellsprache: Englisch
Provides production-grade patterns for Gold layer MERGE operations from Silver to Gold tables. Covers column mapping, schema evolution, SCD Type 1/2 patterns, fact table aggregation, and preventing variable naming conflicts with PySpark functions. Use when…
Quellsprache: Englisch
Delta MERGE deduplication pattern for Gold layer to prevent duplicate source key errors. Use when merging from Silver to Gold tables where Silver may contain duplicate business keys due to incremental DLT streaming, CDC patterns, SCD Type 2 tracking, or…
Quellsprache: Englisch
Pre-merge grain validation patterns for Gold layer fact tables. Use when creating fact table merge scripts to validate that DataFrame grain matches DDL PRIMARY KEY, prevent transaction vs aggregated confusion, and catch grain mismatches before MERGE. Includes…
Quellsprache: Englisch
Canonical pattern for building an agentic tool-calling loop using a Databricks-hosted Foundation Model. Use when creating agent_loop.py or any backend that calls a Foundation Model with function-calling to orchestrate tools.
Quellsprache: Englisch